Doctor of Philosophy (Ph.D.) Major in Electrical Engineering (Entering with Master's Degree)
The Electrical Engineering Ph.D. program in the Department of Engineering Technology at Texas State University is fully conducted online. This carefully crafted curriculum is designed to equip students with skills that are in high demand by potential employers in Texas and the United States, seeking doctoral-level trained engineering managers.
Educational Objectives:
- Sustainability and Resiliency: to enhance students' understanding of sustainability principles in engineering management, considering the social, environmental, and economic impact of decisions.
- Research Specialization: to cultivate students' ability to conduct original and impactful research specialized in project management, operation research, supply chain management, risk management, and sustainable engineering, fostering more in-depth exploration and expertise development.
- Interdisciplinary Approach: to equip students with a strong foundation in both engineering and management disciplines to address complex engineering challenges while incorporating management principles.
- Industry Collaboration: to encourage collaboration with industry and government organizations, providing students with exposure to real-world applications and ensuring the relevance of their research.
- Entrepreneurship and Innovation: to provide students with entrepreneurial skills such as critical thinking, problem-solving, opportunity recognition, and business planning so that they can identify business opportunities, assess risks, and effectively manage resources to deliver successful projects.
Application Requirements
The items listed below are required for admission consideration for applicable semesters of entry during the current academic year. Submission instructions, additional details, and changes to admission requirements for semesters other than the current academic year can be found on The Graduate College's website. International students should review the International Admission Documents page for additional requirements.
- completed online application
- $55 non-refundable application fee
or
- $90 non-refundable application fee for applicants with international credentials
- completed master’s degree in electrical engineering or a closely related discipline, from an accredited college or university
- official transcripts from each institution where course credit was granted.
- competitive GPA
- GRE not required
- resume/CV outlining education, work experience, scholarships/grants, publications/presentations, other accomplishments
- statement of purpose outlining the applicant’s personal history and goals that are relevant for why the applicant wants to pursue this degree at TXST
- three letters of recommendation evaluating your skill and potential in this degree program
- interviews may be conducted with applicants through Zoom, Teams, or other video conferencing applications
TOEFL, PTE, IELTS or Duolingo Scores
Non-native English speakers who do not qualify for an English proficiency waiver:
- official TOEFL iBT scores required with a 78 overall.
- official PTE scores required with a 52 overall.
- official IELTS (academic) scores required with a 6.5 overall and minimum individual module scores of 6.0
- official Duolingo Scores required with a 110 overall.
- official TOEFL Essentials scores required with an 8.5 overall.
This program does not offer admission if these scores are not met.
Additional Information:
The program will admit full-time and part-time students once per year for fall admissions.
Degree Requirements
The Doctor of Philosophy (Ph.D.) degree with a major in Electrical Engineering requires 54 semester credit hours.
Course Requirements
| Code | Title | Hours |
|---|---|---|
| Required Courses | ||
| EE 7300 | Research Methods and Technical Writing in Electrical and Computer Engineering | 3 |
| EE 7331 | AI and Machine Learning for Engineers | 3 |
| MSEC 7301 | Practical Skills in Commercialization and Entrepreneurship | 3 |
| MSEC 7302 | Leadership Skills in Commercialization and Entrepreneurship | 3 |
| Breadth | ||
| Choose 6 hours from the following: | 6 | |
| Machine Learning, AI, Computer and Digital Design | ||
| Advanced Machine Learning and Pattern Recognition | ||
| Image Processing and Computer Vision | ||
| Advanced Digital System Design | ||
| Hardware Acceleration for Machine Learning | ||
| Microelectronics, Nanotechnology and Networks | ||
| Physical Electronics | ||
| Modern Semiconductor Devices | ||
| Wireless and Mobile Networks | ||
| Smart Data Networks | ||
| Smart Energy, Power and Mobility Systems | ||
| Energy Storage and Sustainability | ||
| Artificial Intelligence in Smart Grids | ||
| Mobile and Microgrid Design and Operations | ||
| High and Medium Voltage Power Transmission | ||
| Prescribed Electives | ||
| Choose 12 hours from the following: | 12 | |
| Advanced Machine Learning and Pattern Recognition | ||
| Image Processing and Computer Vision | ||
| High-Performance Computing | ||
| Advanced Parallel Computing | ||
| Cyberspace Security | ||
| Advanced Computer Networking | ||
| Mobile Networks and Computing | ||
| Research in Computer Science | ||
| Service Computing | ||
CS 7389C | ||
CS 7389D | ||
CS 7389E | ||
| Advanced Digital System Design | ||
| Hardware Acceleration for Machine Learning | ||
| Physical Electronics | ||
| Modern Semiconductor Devices | ||
| Energy Storage and Sustainability | ||
| Artificial Intelligence in Smart Grids | ||
| Mobile and Microgrid Design and Operations | ||
| High and Medium Voltage Power Transmission | ||
| Research in Electrical Engineering | ||
| Smart Data Networks | ||
| Wireless and Mobile Networks | ||
| Nanoscale Systems and Devices | ||
| Materials Characterization | ||
| Computational Materials Science | ||
| Microwave & Power Device Physics and Materials | ||
| Thin Film Photovoltaic Devices | ||
| Graph Theory | ||
| Statistics 1 | ||
| Dissertation | ||
| Choose a mimimum of 24 hours from the following: | 24 | |
| Dissertation | ||
| Dissertation | ||
| Dissertation | ||
| Dissertation | ||
| Dissertation | ||
| Dissertation | ||
| Total Hours | 54 | |
Candidacy Criteria
A minimum GPA of 3.0 on all coursework undertaken in the doctoral program is required for admission to candidacy. Grades below a B on any graduate coursework cannot be applied toward the doctoral degree. Incomplete grades must have been cleared before approval for advancement to candidacy can be granted. Before advancing to candidacy, no more than six semester credit hours of dissertation can be taken.
Students will advance to candidacy after they have completed all required and elective coursework (except for dissertation credit hours), passed their comprehensive exam, and successfully defended their dissertation proposal. This should be done by the time the student has completed 60 semester credit hours. If the comprehensive exam is not passed, the student can take a second and final one in the following semester. Students will be dismissed from the program if they do not pass the comprehensive exam the second time.
Candidacy and Dissertation
When all requirements for admission to candidacy have been met, the doctoral program director forwards the Application for Advancement to Candidacy to the Dean of The Graduate College for review and approval. This application form is available on The Graduate College’s website.
No credit will be applied toward a student’s doctoral degree for coursework completed more than five years before the date the student is admitted to candidacy. This time limit applies to course credit earned at Texas State and course credit transferred to Texas State from other institutions.
All doctoral students must complete a dissertation with original research and demonstrate mature scholarship, critical judgment, and familiarity with tools and methods in the chosen area. The dissertation project must adhere to the dissertation proposal and cover the topic approved by the student’s dissertation committee.
After being admitted to candidacy, students must be continuously enrolled for dissertation hours each fall and spring semester until the defense of their dissertation. At least 18 semester credit hours of dissertation research must be taken after having advanced to candidacy. If a student receives supervision on a dissertation during the summer or is graduating in the summer, the student must be enrolled in dissertation hours for the summer. All candidates for graduation must be enrolled in dissertation hours during the semester in which the degree is to be conferred, even if they have already satisfied the minimum dissertation hours.
Comprehensive Exam
Each doctoral student must pass a comprehensive examination. This should be done by the time the student has completed 36 semester credit hours (for students entering with a master’s degree, 60 semester credit hours for those entering with a bachelor’s degree) and can only be done after identifying the dissertation committee, fulfilling the programming requirement, and completing all required courses. Students must pass the comprehensive exam by the time 45 semester credit hours (for students entering with a master’s degree, 60 semester credit hours for those entering with a bachelor’s degree) have been accrued to be dismissed from the program. If the comprehensive exam is not passed, the student can take a second and final one in the following semester. Students will be dismissed from the program if they do not pass the comprehensive exam the second time.
The comprehensive examination for the Ph.D. program in electrical engineering will be an all-encompassing, written examination administered in person under the supervision of the program's Doctoral Advisor. The exam intends to evaluate students' grasp of their coursework and their ability to integrate and apply their knowledge in the field. The comprehensive exam will be structured into six one-hour sections, curated to cover foundational and advanced electrical engineering topics adequately. The exam will be six hours, divided into two distinct segments. The first three hours of the exam will focus on fundamental topics primarily encountered in undergraduate courses in electrical engineering. This segment aims to test the student's grasp of the core principles that are the bedrock of their further studies and research. Following this, the final three hours will shift the focus to advanced topics typically covered in 5000-level courses. This segment assesses students' understanding of complex concepts and ability to apply them in practical scenarios.
Each topic is allocated an hour, providing a robust and comprehensive evaluation of student's knowledge in each area. Students can select six topics that align closely with their research subject area. This approach allows us to tailor the exam to each student's learning journey while maintaining a rigorous assessment standard. The faculty, renowned experts in their respective domains, will prepare each section, ensuring the exam content reflects the field's breadth and depth. This approach allows us to tailor the exam to each student's learning journey while still maintaining a rigorous standard of assessment across the board. The faculty, renowned experts in their respective domains, will prepare each section, ensuring the exam content reflects both the breadth and depth of the field.
The Doctoral Advisor will coordinate all aspects of the exam, from facilitating the drafting of exam sections by faculty members to overseeing the administration and grading of the exam. They will also serve as a point of contact for students throughout the examination process, addressing any concerns and providing guidance as needed. This comprehensive exam is an integral part of the program, designed to ensure our students have a robust understanding of the concepts they have studied and are prepared to undertake high-level research and professional responsibilities in electrical and computer engineering.
Dissertation Proposal and Proposal Defense
Each Ph.D. student must prepare a written dissertation proposal and defend it orally. This should be done by the time the student has completed 36 semester credit hours (for students entering with a master’s degree and 60 hours for students entering with a bachelor’s degree) and after identifying the dissertation committee, passing the comprehensive exam, and completing all required courses and boot camps. Any student who does not defend his/her dissertation proposal by the time 45 semester credit hours or students entering with a master’s degree and 60 hours for students entering with a bachelor’s degree) have been accrued will be dismissed from the program. If the proposal defense is not passed, the student can take a second and final defense in the following long semester. Students will be dismissed from the program if they do not pass the proposal defense the second time.
The proposal must outline the substance and scope of the planned dissertation research and explain its merits. It must include at least an introduction, the methodology to be used, a survey of the relevant literature, and preliminary results that demonstrate the feasibility. The proposal aims to establish that the student has a sufficient grasp of the fundamentals of the chosen dissertation topic to execute the research.
The proposal defense entails a public presentation of the student’s dissertation proposal followed immediately by a closed defense of the proposal attended only by the student and their dissertation committee. The dissertation proposal must be approved by the student’s dissertation advisor and a majority of the remaining members of the dissertation committee. The student’s dissertation committee members must indicate their approval on the Doctoral Dissertation Proposal Form and the Defense of Dissertation Proposal Form. These forms are available on The Graduate College’s website.
A final copy of the dissertation proposal, accompanied by the signed approval forms, must be turned in to the doctoral program director, who will forward them to the dean of The Graduate College for review and final approval.
All doctoral students must complete a dissertation with original research and demonstrate mature scholarship, critical judgment, and familiarity with tools and methods in the chosen area. The dissertation project must adhere to the dissertation proposal and cover the topic approved by the student’s dissertation committee.
Dissertation Advisor
The Ph.D. program director serves as the initial advisor of each student accepted into the program. The director then works with the student and the faculty to identify possible dissertation advisors. By 18 semester credit hours have been accrued, each doctoral student is expected to have secured a qualified dissertation advisor who agrees to advise and mentor the student. The mentoring by the dissertation advisor should include providing regular feedback to students and supervising them throughout the Ph.D. program – specifically in the execution of the dissertation research – and helping them identify short- and long-term career goals—for the Ph.D. Dissertation/Research Advisor Assignment Form must be completed by the student and the dissertation advisor and approved by the Dean of The Graduate College. This form may be downloaded from The Graduate College’s website.
If a student has not identified a willing and qualified dissertation advisor by the time, he/she has accrued 27 semester credit hours, the student will be dismissed from the program.
Dissertation Committee
The Dissertation Committee will consist of 4 members, including the student’s dissertation committee chair who must be a regular graduate faculty member in the program, two other graduate faculty members from the electrical engineering program, and a PhD holder from industry or a government agency or one doctoral graduate faculty from another department or program at Texas State University or from another university. The student’s dissertation committee chair will chair the committee. The student, the dissertation committee chair, and the Dean of The Graduate College will approve the composition of the dissertation committee.
The dissertation defense consists of two parts. The first part is a public presentation of their dissertation research. The second part of the defense immediately follows the public presentation. It is restricted to the student’s dissertation committee participation and entails an oral examination of the dissertation research. Approval of the dissertation requires positive votes from the student’s dissertation advisor and the majority of the remaining members of the dissertation committee. Notice of the defense presentation will be publicly posted at least two weeks in advance.
Dissertation Defense
Once the dissertation has been completed, a final exam (referred to as the dissertation defense) on the dissertation must be conducted. The dissertation defense can only be scheduled once all other academic and program requirements have been fulfilled. A complete dissertation draft must be given to the dissertation committee members at least one month before the defense. However, students are highly encouraged to provide drafts earlier so that the committee members can provide feedback, which the student, in consultation with the dissertation advisor, will address in later drafts to ensure that the dissertation is defendable, and each committee member is satisfied before the dissertation defense takes place.
The dissertation defense consists of two parts. The first part is a public presentation of their dissertation research. The second part of the defense immediately follows the public presentation. It is restricted to the student’s dissertation committee participation and entails an oral examination of the dissertation research. Approval of the dissertation requires positive votes from the student’s dissertation advisor and the majority of the remaining members of the dissertation committee. Notice of the defense presentation will be publicly posted at least two weeks in advance.
If the dissertation defense is not approved, the student can take a second and final dissertation defense in the following long semester. Students who do not pass the dissertation defense the second time will be dismissed from the program.
The dissertation defense results must be recorded in the Dissertation Defense Report Form and submitted to The Graduate College before the Dean of The Graduate College can approve the dissertation. This form can be downloaded from The Graduate College’s website. The student must submit his/her dissertation to The Graduate College for final approval. The guidelines for submission and approval of the dissertation can be obtained from The Graduate College.
Students must pass the dissertation defense by the time 90 semester credit hours have been accrued. The doctoral program will review each student annually to ascertain his/her progress toward the degree and will consult the student’s dissertation advisor and dissertation committee on this matter as needed. Any student who does not pass the dissertation defense by the time 90 semester credit hours accrued will be dismissed from the program.
Doctoral level courses in Electrical Engineering: EE, EMSE
Courses Offered
Electrical Engineering (EE)
EE 7199. Dissertation.
This course examines the principles of original research and advanced technical writing in electrical and computer engineering. Students analyze academic literature and current technologies to identify gaps in existing knowledge. Topics include formulation of research questions, development of experimental or theoretical frameworks, and design of prototypes, simulations, or analytical models under faculty supervision. Students collect and evaluate data to support original findings and assess research validity. The course also addresses ethical considerations and professional standards in scholarly research. Emphasis is placed on producing a dissertation that demonstrates technical rigor and contributes to the field.
1 Credit Hour. 1 Lecture Contact Hour. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
EE 7299. Dissertation.
This course examines the principles of original research and advanced technical writing within the specialized domains of electrical and computer engineering. Students analyze academic literature and current technologies to identify gaps in engineering knowledge. Inquiry focuses on formulating hypotheses and developing experimental or theoretical frameworks to address complex technical problems. Students examine the design and implementation of prototypes, simulation models, or mathematical proofs under the guidance of a doctoral research advisor. Analysis includes the collection and evaluation of data to validate findings. Students also examine the ethical and societal implications of research while adhering to professional standards. The course culminates in the production of a dissertation demonstrating technical rigor and contribution to the field.
2 Credit Hours. 2 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
EE 7300. Research Methods and Technical Writing in Electrical and Computer Engineering.
This course examines the principles of empirical investigation and the methodologies required for advanced research within the electrical and computer engineering domain. Students analyze the systematic processes of formulating research questions and building theoretical frameworks to address complex engineering problems. Inquiry focuses on the techniques of data analysis and evidence construction to evaluate the validity of experimental findings. Students examine the structure of scientific manuscripts and the protocols of the peer-review process for academic publishing. Analysis includes the utilization of digital databases and the ethical attribution of credit for prior work. Students investigate the legal constraints of intellectual property rights and the ethical standards governing professional research conduct. Evaluation of diverse research methodologies identifies the capacities and limitations of specific qualitative and quantitative approaches. Systematic inquiry into technical communication provides a framework for the dissemination of original engineering contributions.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7301. Advanced Digital System Design.
This course examines the principles of digital systems design using hardware description languages and automated design toolchains to evaluate the integration and verification of complex hardware. Students analyze modern design flows, including high-level synthesis (HLS) and register-transfer-level (RTL) synthesis, to determine system performance and resource utilization. Inquiry focuses on the functional role of hardware description languages in simulating and validating modular hardware components in large-scale digital systems. Students examine advanced design methodologies through the implementation of digital architectures on programmable targets, such as Field-Programmable Gate Arrays (FPGA) or Application-Specific Integrated Circuits (ASIC). Analysis includes the study of timing closure, power optimization, and hardware-software co-design within high-performance computing frameworks. Students investigate the relationship between architectural abstractions and physical hardware synthesis. Evaluation of these design frameworks identifies the technical trade-offs between logic density and operational speed. Systematic inquiry into digital systems provides a framework for the development of robust and scalable hardware.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7302. Hardware Acceleration for Machine Learning.
This course examines the design principles of artificial intelligence systems and the architectural frameworks required for deep learning acceleration. Students analyze computing platforms, including CPUs, GPUs, and TPUs, to evaluate performance in neural network training and inference. Inquiry focuses on FPGA architectures and specialized programming models for hardware synthesis. Students examine memory organization and dataflow patterns to optimize processing for large-scale AI models. Analysis includes power consumption strategies at the system and register-transfer level to support energy-efficient acceleration. Students also investigate relationships between algorithmic complexity and hardware constraints. The course emphasizes evaluation of trade-offs between computational throughput and power-area efficiency in machine learning hardware.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7303. Physical Electronics.
This course examines the advanced principles of semiconductor device physics and the operational dynamics of micro- and nano-electronic systems. Students analyze the fundamental laws of quantum mechanics to evaluate the behavior of carriers in confined and periodic potentials. Inquiry focuses on the application of Fermi-Dirac statistics and the density of states to determine carrier concentrations and distribution within energy bands. Students examine charge transport mechanisms in crystalline semiconductors, including drift, diffusion, and scattering processes, to evaluate mobility and conductivity. Analysis includes the study of electronic transitions and the operational physics of optoelectronic devices, such as photodetectors, light-emitting diodes, and semiconductor lasers. Students investigate the relationship between material properties and the functional reliability of solid-state architectures. Evaluation of these physical electronics frameworks identifies the technical constraints of maintaining performance at reduced scales. Systematic inquiry into device physics provides a framework for the development of next-generation electronic and photonic technologies.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7304. Modern Semiconductor Devices.
This course examines the advanced physics and operational dynamics of modern semiconductor architectures within the context of nanoscale integration. Students analyze metal-semiconductor contacts and the physical principles of p-n junction operation to evaluate charge carrier behavior and rectifying characteristics. Inquiry focuses on the electrostatics of MOS capacitors and the functional characteristics of metal-oxide-semiconductor field-effect transistors (MOSFETs). Students examine the impact of scaling and short-channel effects on the performance of contemporary and emerging transistor technologies. Analysis includes the study of optoelectronic devices, including photodetectors, solar cells, and light-emitting structures, to determine their quantum efficiency and spectral response. Students investigate the relationship between material parameters and the functional reliability of solid-state display devices. Evaluation of these semiconductor frameworks identifies the technical challenges of maintaining device control at reduced dimensions. Systematic inquiry into device physics provides a framework for the development of high-performance integrated circuits and photonic systems.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7305. Energy Storage and Sustainability.
This course examines the principles of energy storage mechanisms and the technical frameworks relevant to sustainability. Students analyze electrochemical processes governing storage technologies to evaluate energy density and cycle life across battery chemistries. Inquiry focuses on the operational physics of advanced storage systems and their integration into renewable energy grids. Students examine technical, economic, and policy considerations associated with energy infrastructure transitions across sectors. Analysis includes decentralized power management and socio-economic factors influencing energy system adoption. Students investigate research trends in hybrid storage and thermodynamic limits of emerging technologies. The course emphasizes evaluation of trade-offs between cost, environmental impact, and grid reliability in energy storage systems.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7306. Artificial Intelligence in Smart Grids.
This course examines the integration of artificial intelligence (AI) and machine learning (ML) architectures within modern smart grid infrastructures. Students analyze machine learning algorithms and computational tools to evaluate their application in power system design, operation, and reliability. Inquiry focuses on neural networks and statistical models for energy forecasting and demand response optimization. Students examine smart meter data analytics to assess consumption patterns and distributed energy resource efficiency. Analysis includes nonintrusive load monitoring techniques to identify appliance-level signals. Students investigate the relationship between stochastic data inputs and real-time grid control processes. The course emphasizes evaluation of trade-offs between algorithmic complexity and latency in critical power systems.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7307. Mobile and Microgrid Design and Operations.
This course examines the advanced principles of modeling, control, and security within the context of microgrid design and grid modernization. Students analyze microgrid architectures and the characterization of distributed energy resources to evaluate system performance and integration. Inquiry focuses on the implementation of smart inverters and hierarchical control strategies to determine voltage and frequency stability in both islanded and grid-tied modes. Students examine the mechanics of fault management and the development of resilient microgrids through programmable networks and software-defined technologies. Analysis includes the investigation of reliable networked microgrids and the application of cyber security frameworks to protect critical energy infrastructure. Students investigate the relationship between energy management systems and the dynamic constraints of distributed generation. Evaluation of these microgrid frameworks identifies the technical trade-offs between system autonomy and interconnected reliability. Systematic inquiry into modern grid operations provides a framework for the development of secure and adaptive energy distribution systems.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7308. High and Medium Voltage Power Transmission.
This course examines the principles of electric power transmission and distribution systems within the context of grid modernization and distributed energy integration. Students analyze the structural components and typical topologies of distribution grids to evaluate operational strategies for high and medium voltage networks. Inquiry focuses on the integration of distributed generation, including photovoltaics and wind, and the role of storage and controllable loads in system stability. Students examine the mathematical frameworks of power flow analysis and the functional characteristics of distribution transformers. Analysis includes the study of electric load modeling and the development of control methodologies to address variable demand profiles. Students investigate the relationship between transmission efficiency and the deployment of smart grid technologies. Evaluation of these power systems identifies the technical trade-offs between grid reliability and the increasing penetration of intermittent energy resources. Systematic inquiry into distribution provides a framework for the development of resilient electrical infrastructures.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7331. AI and Machine Learning for Engineers.
This course examines the principles of artificial intelligence and machine learning architectures through the analysis of supervised, unsupervised, and semi-supervised learning paradigms. Students analyze the functional characteristics of deep neural networks, convolutional neural networks, and recurrent neural networks to evaluate their efficacy in engineering applications. Inquiry focuses on reinforcement learning frameworks, including Markov Decision Processes, Q-learning, and policy gradients, for dynamic decision-making problems. Students examine the mathematical foundations of model optimization and generalization to assess predictive reliability. Analysis includes recent advancements in deep learning and their integration into engineering platforms. The course emphasizes trade-offs between computational overhead and inference accuracy in AI systems.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7354. Advanced Flexible Electronics.
This course examines the materials systems, fabrication processes, and device physics of advanced flexible electronic architectures within the context of contemporary research. Students analyze the structural and electronic properties of semiconductor materials, including amorphous silicon, nanocrystalline silicon, and organic polymers. Inquiry focuses on the implementation of solution-cast films from carbon nanotubes, graphene, and other two-dimensional materials to determine mechanical and electrical performance. Students examine the operational principles of high-speed transistors, switches, and photovoltaics designed for non-rigid substrates. Analysis includes the study of integration strategies for flexible communication devices and the impact of mechanical strain on carrier transport. Students investigate the relationship between material deposition techniques and the functional reliability of conformable electronics. Evaluation of these emerging technologies identifies the technical challenges of maintaining high-frequency performance in flexible systems. Systematic inquiry into flexible electronics provides a framework for the development of next-generation wearable and portable hardware.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7359. Research in Electrical Engineering.
This course examines the principles of foundational research and experimental design for doctoral students within the electrical engineering discipline. Students analyze specialized technical literature and current engineering methodologies to define significant research problems. Inquiry focuses on the development of preliminary theoretical models and the implementation of initial simulation or laboratory experiments. Students examine the integration of advanced mathematical tools and physical principles to evaluate the feasibility of novel engineering solutions. Analysis includes the systematic collection of data and the critical assessment of preliminary findings under the guidance of a doctoral research advisor. Students investigate the relationship between established engineering standards and emerging technological trends to formulate a comprehensive research direction. Evaluation of these investigative techniques identifies the technical requirements for advanced doctoral-level inquiry and future dissertation development. Systematic inquiry into electrical engineering research provides a framework for the advancement of innovative hardware and software systems.
3 Credit Hours. 0 Lecture Contact Hours. 12 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
EE 7372. Wireless and Mobile Networks.
This course examines the principles and architectural frameworks of modern wireless and mobile networking technologies. Students analyze wireless signal propagation characteristics and stochastic modeling to evaluate communication link reliability across diverse environments. Inquiry focuses on the implementation of advanced coding, modulation, and link-layer techniques to optimize data transmission. Students examine the mechanics of multiple access schemes and protocols across local, personal, and wide-area networks, including 5G and 6G paradigms. Analysis includes the study of sensor networks and low-power wide-area networks (LPWAN) with an emphasis on energy-aware and energy-harvesting protocols. Students investigate the dynamics of mobile ad-hoc networks and specialized variants such as unmanned aerial networks and vehicular ad-hoc networks. Evaluation of these networking frameworks identifies the integration of Internet of Things architectures and machine learning algorithms to enhance network autonomy. Systematic inquiry into mobile communication theory provides a framework for the development of next-generation intelligent wireless infrastructures.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7374. Smart Data Networks.
This course examines the design principles of smart data networks and the integration of artificial intelligence (AI) within traditional network architectures. Students analyze the evolution of the protocol stack to evaluate how autonomous algorithms optimize performance beyond conventional boundaries. Inquiry focuses on the implementation of AI-enhanced medium access control (MAC) protocols and intelligent routing strategies. Students examine the role of machine learning in congestion control and transport protocols to ensure efficient multimedia streaming. Analysis includes the study of traffic characterization and the development of predictive models for network management. Students investigate the relationship between advanced networking paradigms, such as the Internet of Things (IoT) and digital twins, and system-wide scalability. Evaluation of these intelligent frameworks identifies the technical trade-offs between computational complexity and real-time responsiveness. Systematic inquiry into smart networking provides a framework for the development of adaptive and self-healing internet infrastructures.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
EE 7399. Dissertation.
This course examines the principles of original research and advanced technical communication within the specialized domains of electrical and computer engineering. Students analyze contemporary engineering challenges and existing scholarly literature to define a significant contribution to the field. Inquiry focuses on the development of rigorous theoretical frameworks and the implementation of sophisticated experimental methodologies to address complex technical problems. Students examine the design and validation of novel prototypes, simulation architectures, or mathematical proofs under the guidance of a dissertation advisor. Analysis includes the systematic evaluation of data and the critical assessment of research outcomes to ensure technical accuracy and scholarly integrity. Students investigate the broader impacts and ethical considerations of their technological innovations. Evaluation of these research processes supports the synthesis of a formal dissertation that demonstrates mastery of specialized engineering concepts. Systematic inquiry into doctoral-level research provides a framework for the advancement of state-of-the-art engineering solutions.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
EE 7599. Dissertation.
This course examines the principles of original research and advanced technical communication within the specialized domains of electrical and computer engineering. Students analyze contemporary engineering challenges and existing scholarly literature to define a significant contribution to the field. Inquiry focuses on the development of rigorous theoretical frameworks and the implementation of sophisticated experimental methodologies to address complex technical problems. Students examine the design and validation of novel prototypes, simulation architectures, or mathematical proofs under the guidance of a dissertation advisor. Analysis includes the systematic evaluation of data and the critical assessment of research outcomes to ensure technical accuracy and scholarly integrity. Students investigate the broader impacts and ethical considerations of their technological innovations. Evaluation of these research processes supports the synthesis of a formal dissertation that demonstrates mastery of specialized engineering concepts. Systematic inquiry into doctoral-level research provides a framework for the advancement of state-of-the-art engineering solutions.
5 Credit Hours. 5 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
EE 7699. Dissertation.
This course examines the principles of original research and advanced technical communication within the specialized domains of electrical and computer engineering. Students analyze contemporary engineering challenges and existing scholarly literature to define a significant contribution to the field. Inquiry focuses on the development of rigorous theoretical frameworks and the implementation of sophisticated experimental methodologies to address complex technical problems. Students examine the design and validation of novel prototypes, simulation architectures, or mathematical proofs under the guidance of a dissertation advisor. Analysis includes the systematic evaluation of data and the critical assessment of research outcomes to ensure technical accuracy and scholarly integrity. Students investigate the broader impacts and ethical considerations of their technological innovations. Evaluation of these research processes supports the synthesis of a formal dissertation that demonstrates mastery of specialized engineering concepts. Systematic inquiry into doctoral-level research provides a framework for the advancement of state-of-the-art engineering solutions.
6 Credit Hours. 6 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
EE 7999. Dissertation.
This course examines the principles of original research and advanced technical communication within the specialized domains of electrical and computer engineering. Students analyze contemporary engineering challenges and existing scholarly literature to define a significant contribution to the field. Inquiry focuses on the development of rigorous theoretical frameworks and the implementation of sophisticated experimental methodologies to address complex technical problems. Students examine the design and validation of novel prototypes, simulation architectures, or mathematical proofs under the guidance of a dissertation advisor. Analysis includes the systematic evaluation of data and the critical assessment of research outcomes to ensure technical accuracy and scholarly integrity. Students investigate the broader impacts and ethical considerations of their technological innovations. Evaluation of these research processes supports the synthesis of a formal dissertation that demonstrates mastery of specialized engineering concepts. Systematic inquiry into doctoral-level research provides a framework for the advancement of state-of-the-art engineering solutions.
9 Credit Hours. 9 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
Materials Science, Engineering and Commercialization (MSEC)
MSEC 7100. Doctoral Assistant Development.
This course examines the roles, responsibilities, and professional practices associated with serving as a doctoral teaching assistant. Course focus rotates among three core themes: (1) classroom management and instructional support practices, (2) research‑informed teaching methods, learning objectives, and assessment strategies, and (3) teaching and research integrity, including the responsible conduct of research as defined by federal agencies such as NSF, NIH, and USDA. The course also addresses institutional policies, ethical considerations, and professional expectations relevant to supporting instruction in undergraduate and graduate settings. This course does not earn graduate degree credit.
1 Credit Hour. 1 Lecture Contact Hour. 0 Lab Contact Hours.Course Attribute(s): Graduate Assistantship|Exclude from Graduate GPA
Grade Mode: Leveling/Assistantships
MSEC 7101. Commercialization Forum.
This course introduces students to the principles and practices of innovation translation, intellectual property management, technology transfer, and business development in science and engineering. Students engage with entrepreneurs, licensing professionals, and commercialization experts to explore how discoveries move from the laboratory to real-world applications. Topics include patenting strategies, startup formation, licensing agreements, funding mechanisms, and market assessment. Emphasis is placed on integrating technical knowledge with entrepreneurial and managerial decision-making to evaluate and advance emerging technologies in academic, industrial, and commercial settings. Repeatable two times for credit.
1 Credit Hour. 1 Lecture Contact Hour. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Standard Letter
MSEC 7102. MSEC Seminar.
This course exposes students to current research topics and technical challenges in materials science and engineering through a weekly seminar series featuring speakers from academia, industry, and government. Students critically examine emerging research, analyze scientific methodologies, and discuss implications for materials science practice and innovation. The course emphasizes the development of professional skills, including scientific communication, research critique, and engagement with experts, preparing students to integrate insights from cutting-edge research into their dissertation work, interdisciplinary collaborations, and future careers in science and engineering. Repeatable two times for credit.
1 Credit Hour. 1 Lecture Contact Hour. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Standard Letter
MSEC 7103. Research in Materials Science, Engineering, and Commercialization.
This course provides doctoral students in Materials Science, Engineering, and Commercialization with structured research experience prior to advancement to candidacy. Under the supervision of a PhD research advisor, students examine research problems relevant to their field and engage in scholarly inquiry supporting the development of a dissertation research agenda. The course emphasizes research planning, literature analysis, methodological development, and preliminary data collection and interpretation. Students evaluate research progress and refine research questions in preparation for the doctoral candidacy examination. This course is repeatable for doctoral credit across MSEC 7103, 7203, and 7303 for a total of up to six credit hours.
1 Credit Hour. 1 Lecture Contact Hour. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
MSEC 7199. Dissertation.
This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.
1 Credit Hour. 1 Lecture Contact Hour. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
MSEC 7203. Research in Materials Science, Engineering, and Commercialization.
This course provides doctoral students in Materials Science, Engineering, and Commercialization with structured research experience prior to advancement to candidacy. Under the supervision of a PhD research advisor, students examine research problems relevant to their field and engage in scholarly inquiry supporting the development of a dissertation research agenda. The course emphasizes research planning, literature analysis, methodological development, and preliminary data collection and interpretation. Students evaluate research progress and refine research questions in preparation for the doctoral candidacy examination. This course is repeatable for doctoral credit across MSEC 7103, 7203, and 7303 for a total of up to six credit hours.
2 Credit Hours. 2 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
MSEC 7299. Dissertation.
This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.
2 Credit Hours. 2 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
MSEC 7301. Practical Skills in Commercialization and Entrepreneurship.
This course analyzes core principles underlying the commercialization of innovation as the first component of a two-part series. Students evaluate intellectual property regimes, technology transfer mechanisms, licensing approaches, capital formation strategies, governance structures, project management systems, and statistical process control methodologies. Using business plan development as an integrative analytical tool, participants examine strategic alignment, financial feasibility, and operational scalability. The course prioritizes systematic inquiry, application of quantitative and qualitative frameworks, and critical evaluation of commercialization pathways across institutional and entrepreneurial environments.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7302. Leadership Skills in Commercialization and Entrepreneurship.
This course analyzes the processes involved in commercializing technology-driven ventures within a structured business planning framework. Students evaluate intellectual property regimes, licensing mechanisms, capital formation strategies, governance models, project management methodologies, and statistical approaches to quality and process control. Using applied exercises and comparative case studies, participants examine how legal, financial, and operational variables influence venture design and scalability. The course emphasizes critical assessment of commercialization strategies and the integration of multidisciplinary tools to support evidence-based business decision-making. Prerequisite: MSEC 7301 with a grade of "B" or better.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7303. Research in Materials Science, Engineering, and Commercialization.
This course provides doctoral students in Materials Science, Engineering, and Commercialization with structured research experience prior to advancement to candidacy. Under the supervision of a PhD research advisor, students examine research problems relevant to their field and engage in scholarly inquiry supporting the development of a dissertation research agenda. The course emphasizes research planning, literature analysis, methodological development, and preliminary data collection and interpretation. Students evaluate research progress and refine research questions in preparation for the doctoral candidacy examination. This course is repeatable for doctoral credit across MSEC 7103, 7203, and 7303 for a total of up to six credit hours.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
MSEC 7304. Collaborative Research/Commercialization Experience.
This course allows Ph.D. level graduate students to initiate, conduct, and participate in a collaborative research or commercialization experience with graduate faculty, either internally or externally, in addition to research conducted under MSEC 7103, MSEC 7303, MSEC 7199, and MSEC 7399. This course recognizes the collaborative nature of the scientific investigation and commercialization enterprise and is designed to support meaningful research engagement under the guidance of a dissertation chair and a collaborating mentor. Repeatable for doctoral credit up to 6 hours.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Credit/No Credit
MSEC 7310. Nanoscale Systems and Devices.
This course provides an in-depth examination of physical phenomena governing nanoscale systems and their implications for device performance. Topics include electronic, photonic, and mechanical behavior in nanoscale structures, as well as transport, confinement, and surface effects unique to reduced dimensions. Applications span nanoelectronic devices, biomedical systems, micro- and nanoscale manipulation, adaptive optics, and microfluidic technologies. Emphasis is placed on linking fundamental nanoscale physics to device design, functionality, and performance, and on analyzing how material properties and structure influence behavior in advanced nanoscale systems.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7311. Materials Characterization.
This course provides a comprehensive introduction to advanced materials characterization techniques used to analyze structure, composition, and properties across multiple length scales. Topics include electron microscopy methods such as transmission electron microscopy (TEM), scanning electron microscopy (SEM), scanning probe techniques including scanning tunneling microscopy (STM) and atomic force microscopy (AFM), and optical methods such as confocal microscopy. Diffraction-based techniques, including X-ray and neutron diffraction, are also covered, with emphasis on structure determination, phase identification, texture analysis, and small-angle scattering. Emphasis is placed on interpreting characterization data and relating measurements to material structure and performance.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Dif Tui- Science & Engineering
Grade Mode: Standard Letter
MSEC 7315. Quantum Mechanics for Materials Scientists.
This course provides a quantum-mechanical foundation for the study of materials at the nanometer and atomic scales. Topics include core principles of quantum physics; stationary states of one-dimensional model potentials; symmetry considerations; interactions between matter and electromagnetic radiation; scattering and reaction rate theory; spectroscopy; chemical bonding and molecular orbital theory; quantum descriptions of solids; perturbation theory; and nuclear magnetic resonance. Emphasis is placed on applying quantum-mechanical concepts to the analysis and interpretation of material structure, properties, and characterization techniques relevant to advanced materials research.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7320. Nanocomposites.
This course examines the structure, processing, and properties of nanocomposite materials. Topics include the characteristics of nanoparticles used in nanocomposites; surface modification techniques; methods for nanoparticle dispersion and nanocomposite fabrication; major classes of nanocomposites; structure–property relationships; analytical methods for composite characterization; and representative engineering applications. Emphasis is placed on the scientific principles and theoretical models that explain the unique mechanical, thermal, electrical, and functional behaviors of nanocomposite systems. Students will evaluate processing–structure–property relationships and interpret characterization data relevant to research and development of advanced multifunctional materials.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7325. Principles of Technical Project Management.
This course provides technical project management principles to effectively plan, lead, and manage a complex technical project. The content of the course includes understanding of project roles and responsibilities, project life cycles and processes, and project management planning, including scope, cost, quality, schedule, and risks. Students will develop a project management plan for an independent technical project. The course content is designed to prepare students for certification in project management from the Project Management Institute.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7330. Computational Material Science.
This course introduces computational approaches used to model and predict the structure and properties of materials across multiple length scales. Topics include quantum-mechanical modeling and density functional theory; force-field-based atomistic simulations; energy minimization and molecular dynamics; mesoscale modeling methods; and prediction of thermodynamic, structural, vibrational, magnetic, and electrical properties. Students examine crystal structures, phase equilibria, and electronic structure using modern computational tools and interpret simulation results in the context of experimental observations. Emphasis is placed on applying computational methods to support materials design, characterization, and dissertation-level research.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7340. Biomaterials and Biosensors.
This course provides an in depth examination of the design, function, and performance of biomaterials and biosensors used in biomedical applications. Students explore material properties, physiological responses, transduction mechanisms, and fabrication approaches involved in creating clinically relevant devices. The course integrates analysis of polymers, hydrogels, nanomaterials, and inorganic materials with applications in drug delivery, tissue engineering, medical diagnostics, and sensing. Through lectures, discussions, and independent research activities, students will develop the ability to evaluate biomaterial systems, interpret performance criteria, and understand regulatory, ethical, and translational considerations in biomedical device development.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7350. Frontiers of Nanoelectronics.
This course introduces the operating principles of nanoscale electronic and optoelectronic devices, with emphasis on how reduced dimensions and quantum effects influence device behavior. Topics include quantum confinement in low-dimensional systems such as quantum wells, wires, and dots, as well as molecular and emerging nanoelectronic devices. The course examines how advanced nanofabrication techniques enable these technologies and explores their impact on device performance. Emphasis is placed on linking quantum mechanical phenomena, material properties, and fabrication approaches to the design and analysis of next-generation nanoelectronic systems.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7355. Fluid Flow in Porous Media.
This course examines the theory and analysis of fluid transport in heterogeneous porous media. Governing equations for fluid flow and mass transport are developed and applied using analytical and numerical solution methods to predict flow behavior and transport processes. Applications include natural and engineered porous systems such as soils, rocks, concrete, and biological materials. Emphasis is placed on interpreting flow fields, permeability, and transport mechanisms and on using porous media principles to analyze, design, and optimize materials and systems relevant to materials science and engineering research.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7360. Nanomaterials Processing.
This course examines the processing and fabrication of nanomaterials and semiconductor devices, with emphasis on nanoscale phenomena and manufacturing techniques. Topics include properties of electronic materials, thin film deposition methods, etching processes, lithography, and related device physics. Students are introduced to fabrication workflows and characterization techniques used in nanomanufacturing environments, including cleanroom practices. Emphasis is placed on understanding how processing conditions influence material structure, properties, and device performance, and on integrating fabrication and characterization approaches to support research and development of nanoscale systems. Prerequisite: MSEC 7401 with a grade of "C" or better.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7370. Advanced Polymer Science.
This course examines advanced topics in polymer science with emphasis on polymer processing and characterization, testing, and applications. Topics include shape memory polymers, polymer lithography, nano and microfabrication, polymer additives, reactions of polymers, high-temperature polymers, polymers in biomedical applications, natural polymers, and electroactive polymers. Emphasis is placed on understanding the molecular and microstructural mechanisms that govern polymer performance and on analyzing structure–processing–property relationships relevant to advanced engineering applications.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7375. Structure and Properties of Alloys.
This course provides an advanced examination of engineering alloys, focusing on their structures, properties, and strengthening mechanisms across ferrous, nonferrous, and emerging alloy systems. The course also examines how processing conditions influence microstructure, performance, and mechanical behavior. Emphasis is placed on the analytical evaluation of alloy systems through metallurgical principles, phase transformations, and application-driven examples. Topics include equilibrium and non-equilibrium transformation products, alloy design considerations, and relationships among composition, processing, microstructure, and material properties in advanced engineering applications.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7380. Advanced Infrastructure Materials.
This course examines advanced infrastructure materials used in civil engineering, including cement concrete, asphalt concrete, wood, and steel. The course analyzes the composition of cement concrete with a focus on how raw ingredients influence fresh and hardened material properties. Additional infrastructure materials are evaluated through comparative discussion to highlight differences in behavior and application. Students apply analytical reasoning to infrastructure materials–related problems using advanced analytical and simulation tools. Emphasis is placed on understanding material behavior through data interpretation, modeling, and quantitative analysis.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Dif Tui- Science & Engineering
Grade Mode: Standard Letter
MSEC 7395B. Thin Film Photovoltaic Devices.
This course examines the materials science and device physics underlying photovoltaic energy conversion, with emphasis on thin film solar cell technologies. Topics include the photovoltaic effect, photon absorption, carrier generation and recombination, electron and hole transport, pn-junction behavior, and charge separation mechanisms. Students study monocrystalline, thin film, and III–V photovoltaic materials and analyze performance losses and efficiency limitations. Emphasis is placed on connecting material structure and electronic properties to device performance and on interpreting experimental characterization and performance metrics relevant to modern photovoltaic research and development. Prerequisite: MSEC 7401 and MSEC 7402 with grades of "B" or better.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing|Topics
Grade Mode: Standard Letter
MSEC 7395D. Polymer Characterization and Processing.
This course examines polymeric materials which are widely used in structural, electronic, biomedical, and energy applications. Their performance depends strongly on molecular structure, processing conditions, and resulting microstructure. The course provides doctoral students with the fundamental knowledge and analytical tools required to characterize polymer structure and properties and to understand how processing methods influence material behavior. By integrating characterization techniques—such as molecular weight analysis, thermo-mechanical testing, X-ray scattering, and spectroscopy—with polymer rheology and processing methods, the course prepares students to analyze structure–processing–property relationships in polymer systems. The course supports dissertation research involving polymeric and composite materials and strengthens interdisciplinary training in advanced materials characterization and manufacturing. Prerequisite: MSEC 7370 with a grade of "B" or better.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing|Topics
Grade Mode: Standard Letter
MSEC 7395H. Environmental Chemistry.
This course provides an advanced study of environmental chemistry with emphasis on aquatic systems and applications in materials science and engineering. Topics include principles of geochemistry and atmospheric chemistry as they relate to environmental processes, pollutant behavior, and monitoring and control strategies. The course also examines the principles and applications of green chemistry in the design of sustainable materials, products, and processes. Emphasis is placed on understanding chemical transformations in natural and engineered systems and applying this knowledge to address environmental challenges relevant to materials research and development.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing|Topics
Grade Mode: Standard Letter
MSEC 7395J. Advanced Concrete Materials and Durability.
This course examines Portland cement concrete materials and alternative material systems used in building and transportation infrastructure. Students analyze the physical, chemical, and mechanical properties of cement, aggregates, and chemical and mineral admixtures. Topics include mixture proportioning, concrete microstructure, durability mechanisms, long-term performance, dimensional stability, and deterioration processes. The course evaluates durability prediction methods, modeling approaches, and multi-scale assessment techniques. Alternative cementitious systems are studied through comparative analysis of material behavior and performance under different exposure conditions. Emphasis is placed on understanding material selection, testing methodologies, and performance-based evaluation.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing|Topics
Grade Mode: Standard Letter
MSEC 7395M. Semiconductor Devices and Processing.
This course examines the principles and processes underlying semiconductor device fabrication, with emphasis on both silicon and compound semiconductor systems. Topics include carrier transport, doping mechanisms, and defect engineering, as well as fabrication techniques such as photolithography, etching, ion implantation, and epitaxial growth. Students study the formation of junctions and microstructures required for micro- and nanoscale devices, along with Ohmic contacts and device integration strategies. Laboratory projects and seminar presentations provide experience in applying fabrication concepts and interpreting device performance in conventional and emerging electronic and optoelectronic systems. Prerequisite: MSEC 7401 with a grade of "B" or better.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing|Topics
Grade Mode: Standard Letter
MSEC 7395O. Modern Concepts in Materials Science.
This course provides an overview of fundamental concepts used to describe and predict the structure and properties of engineering materials. Topics include atomic structure and bonding, crystallography, diffraction principles, defects, solid solutions, and phase equilibria. Emphasis is placed on understanding structure–property relationships across major classes of materials, including metals, ceramics, polymers, electronic materials, and composites. The course prepares students to apply core materials science principles to analyze material behavior and supports those without prior formal training in materials science in advancing to graduate-level coursework and research.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing|Topics
Grade Mode: Standard Letter
MSEC 7395P. Optical Properties of Solids.
This course examines the optical properties of solid materials, including electronic and vibrational transitions in inorganic and organic systems, thin films, and multilayer structures. Topics include interactions among electrons, phonons, and photons, and their influence on optical behavior. Students study optical characterization techniques such as UV/Vis spectroscopy, Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy, ellipsometry, photoluminescence, and X-ray fluorescence. Emphasis is placed on interpreting optical spectra to determine material properties and on applying spectroscopic methods to analyze and optimize materials for electronic and optoelectronic applications.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing|Topics
Grade Mode: Standard Letter
MSEC 7395Q. Scanning Probe Microscopy and Nanoscience.
This course introduces fundamental topics in nanoscience, including nanomechanics, nanoelectronics, and nano-optics, using scanning probe microscopy (SPM) as a central analytical tool for studying materials at the nanoscale. Students examine the physical principles underlying major SPM techniques and explore how these methods are applied to measure structural, electrical, and optical properties of nanostructures. The course also covers instrumentation design, signal acquisition, and data interpretation, providing students with both theoretical understanding and practical familiarity with SPM operation relevant to research in nanoscience and nanotechnology.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing|Topics
Grade Mode: Standard Letter
MSEC 7399. Dissertation.
This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.
3 Credit Hours. 3 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
MSEC 7401. Fundamentals of Material Science and Engineering.
This course provides a comprehensive foundation in the fundamental principles of materials science and engineering. Topics include atomic and electronic structure, crystallography, defects, thermodynamics and kinetics, phase diagrams, diffusion, and phase transformations. Additional topics include conservation laws, continuum mechanics, and statistical models relevant to materials behavior. Emphasis is placed on understanding the relationships among structure, processing, and properties in materials systems and on applying fundamental principles to analyze and predict material behavior in engineering applications.
4 Credit Hours. 4 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7402. Advanced Materials Science and Engineering Concepts.
This course builds on fundamental materials science principles to examine advanced concepts governing the behavior of materials. Topics include quantum mechanical foundations of solids, electronic structure, lattice vibrations, magnetism, semiconductors, nanostructures, mesoscopic phenomena, and superconductivity. The course also explores recent advances in emerging materials systems. Emphasis is placed on understanding how quantum and solid-state physics principles influence material properties and functionality, particularly in electronic, photonic, and advanced materials applications. Prerequisite: MSEC 7401 with a grade of "C" or better.
4 Credit Hours. 4 Lecture Contact Hours. 0 Lab Contact Hours.Grade Mode: Standard Letter
MSEC 7599. Dissertation.
This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.
5 Credit Hours. 5 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
MSEC 7699. Dissertation.
This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.
6 Credit Hours. 6 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
MSEC 7999. Dissertation.
This course supports the completion of original, independent research in materials science, engineering, and commercialization under the direct supervision of the student’s PhD research advisor. Students engage in the development, execution, and documentation of doctoral-level research that contributes new knowledge to the materials science, engineering, and commercialization discipline. Continuous enrollment during long semesters ensures sustained scholarly progress, faculty mentorship, and academic oversight throughout the dissertation research and writing process. This course is a required component of the PhD with a major in materials science, engineering, and commercialization.
9 Credit Hours. 9 Lecture Contact Hours. 0 Lab Contact Hours.Course Attribute(s): Exclude from 3-peat Processing
Grade Mode: Credit/No Credit
