Maryam EghbaliZarch
4 months ago
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Fully Funded PhD Opportunity in Health Systems Optimization, Industrial Engineering, and Healthcare Analytics Kennesaw State University in United States
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded PhD/GRA position beginning Fall 2026 with competitive financial support and a tuition waiver.
Deadline
Expired
Country
United States
University
Kennesaw State University

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About this position
Fully funded PhD opportunity at Kennesaw State University in the Health Systems Optimization (HSOpt) Lab, starting Fall 2026.
The position is focused on health systems optimization, healthcare delivery, modeling and analytics, and related work in industrial engineering, operations research, data-driven optimization, Markov modeling, and simulation. The lab is seeking a highly motivated PhD student to contribute to research on improving healthcare systems using quantitative and computational methods.
Eligibility highlights: applicants should have an MS in Industrial Engineering, operations research, or a related field. Required skills include familiarity with Python, R, or Julia, optimization software, strong academic preparation, and strong writing, critical thinking, and analytical skills. Preferred experience includes data science, reinforcement learning, large language models, and publications in peer-reviewed journals or conference proceedings.
Funding: the post states that the selected student will receive competitive financial support and a tuition waiver.
How to apply: submit a CV, transcripts, and a research statement using the provided application form. Applications are reviewed on a rolling basis until the position is filled.
Supervisor/announcer: Maryam EghbaliZarch, Assistant Professor at Kennesaw State University.
Funding details
Fully funded PhD/GRA position beginning Fall 2026 with competitive financial support and a tuition waiver.
What's required
Applicants should have an MS in Industrial Engineering, operations research, or a related field. Required experience includes knowledge of data-driven optimization, Markov modeling, and simulation, plus familiarity with programming languages such as Python, R, or Julia and optimization software. A strong academic background, excellent writing, critical thinking, and analytical skills are expected. Preferred qualifications include prior knowledge of data science, reinforcement learning, large language models, and publications in peer-reviewed journals or conference proceedings.
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