Two Fully Funded PhD Assistantships in Data-driven Optimization and Analytics at the University of Texas at San Antonio

Two Fully Funded PhD Assistantships in Data-driven Optimization and Analytics at the University of Texas at San Antonio

Two Fully Funded PhD Assistantships in Data-driven Optimization and Analytics at the University of Texas at San Antonio are available for prospective Phd students.


The Department of Mechanical Engineering (ME) at The University of Texas at San Antonio (UTSA) has an opening for two highly motivated Ph.D. student positions focused on data-driven optimization and analytics. The students are expected to start either in Fall 2022 or Spring 2023. The fully-funded assistantships will cover tuition and fees as well as offer a competitive stipend.

About ME at UTSA:

UTSA is a Tier 1 research university having the Carnegie R1 classification. The Mechanical Engineering Department at UTSA has a strong research and teaching program in the areas of systems engineering, operations research, and big data analytics with several well-known faculty members.

The ME Department has close ties with several nationally recognized research institutes (e.g., Cybersecurity Manufacturing Innovation Institute) hosted by UTSA and has strong collaboration with multiple U.S. Department of Energy (DOE) Laboratories including Sandia, Oak Ridge, and Idaho National Laboratories.

Successful candidates will have the opportunity to closely work with these institutes and the DOE laboratories. UTSA also has strong ties with multiple large companies and research institutes located in San Antonio including USAA, UT Health San Antonio, and Texas Biomedical Research Institute. San Antonio is the second-largest city in the state of Texas and the seventh-largest city in the United States, offering numerous job opportunities to UTSA students.

Description of the Two Fully Funded PhD Assistantships

Students will work with Dr. Tanveer Hossain Bhuiyan in research projects focused on developing data-driven mathematical programming models (incorporates machine learning models) and efficient solution approaches (e.g., linearization of nonconvex models, design/develop exact, approximate, and heuristic algorithms) to solve complex decision-making problems. Research focus also includes developing machine learning and big data analytics methods using high-performance computing environments to identify patterns in big data.

Successful candidates will have the opportunity to apply these methods in a variety of emerging application areas including; however not limited to the security of smart systems (e.g., smart manufacturing, smart grid systems) against cyber-physical attacks, sustainable and electrified transportation infrastructure (e.g., aerial drones and electric vehicles), and resiliency of complex networks (e.g., power grid with renewable energy resources). To learn more about Dr. Bhuiyan’s research, please visit or contact Dr. Bhuiyan.

Required Qualifications:

  • A B.S. or M.S. degree in Industrial Engineering, Mathematics, Operations Research, Management
    Science, or a closely related discipline.
  • Strong background in mathematical modeling and statistics.
  • Preference will be given to the candidates having strong competence in a programming language
    (Python preferred).
  • Experience in machine learning is a plus.
  • Meet the UTSA graduate school admission requirements:

How to Apply

To apply for these positions, these are the supporting documents:

  • a detailed CV,
  • a brief statement (maximum two pages) describing your research interest, experience, and future goals, and,
  • one sample publication (if available)
    please email the above-mentioned to Dr. Tanveer Hossain Bhuiyan at [email protected].

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Read Also: Two PhD Positions in Data-driven and Learning Control at the University of British Columbia

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