Fully Funded PhD in Health Systems Optimization – Kennesaw State University, USA

Kennesaw State University

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Description

The Health Systems Optimization (HSOpt) Lab at Kennesaw State University is excited to announce a fully funded Ph.D. position for motivated graduate students. This Graduate Research Assistant role will begin in Fall 2026. The lab is led by Assistant Professor Maryam EghbaliZarch, a faculty member in the Department of Industrial and Systems Engineering. HSOpt Lab focuses on advancing research in healthcare systems through innovative approaches. Selected candidates will have the opportunity to engage in cutting-edge projects. Research areas include sequential decision-making under uncertainty, a critical component of complex healthcare operations. Candidates will also explore multi-objective optimization to balance competing healthcare priorities. Data analytics plays a central role in the lab’s methodology. Machine learning techniques will be applied to improve medical decision-making and patient outcomes. The lab fosters collaboration and interdisciplinary research among students and faculty. Candidates will gain hands-on experience with real-world healthcare data and systems. This position is ideal for those passionate about combining engineering, data science, and healthcare to drive meaningful impact. 

Responsibilities

Key Responsibilities

Students joining the Health Systems Optimization (HSOpt) Lab will:

1. Conduct cutting-edge research in healthcare delivery optimization and decision analytics.
2. Develop and implement mathematical and computational models to solve real-world healthcare challenges.
3. Collaborate with interdisciplinary teams across engineering, data science, and healthcare domains.
4. Analyze large and complex datasets using AI, machine learning, and simulation techniques.
5. Contribute to academic publications, conference presentations, and technical reports.
6. Assist in mentoring undergraduate students and supporting lab activities.
7. Participate actively in lab meetings, seminars, and collaborative research discussions. 

Qualification

Required Qualifications

Applicants should possess: 

1. Applicants should hold a Master’s degree in Industrial Engineering, Operations Research, or a closely related field.
2. Candidates must know data-driven optimization techniques applicable to complex systems.
3. An understanding of Markov modeling is essential for research in sequential decision-making under uncertainty.
4. Experience with simulation methods and modeling real-world processes is expected.
5. Familiarity with programming languages such as Python, R, or Julia is required.
6. Candidates should be comfortable using relevant optimization software and computational tools.
7. A strong academic record demonstrating high performance in coursework and research is necessary.
8. Excellent writing skills are required for preparing reports, publications, and proposals.
9. Critical thinking and analytical skills are essential for solving complex engineering problems.
10. Applicants must demonstrate the ability to work independently with minimal supervision.
11. A high degree of self-motivation and intellectual curiosity is expected.
12. Strong problem-solving capability and adaptability in dynamic research environments are required. 

Preferred Qualifications:

• Prior experience in data science applications in engineering or healthcare is advantageous.
• Knowledge of reinforcement learning techniques for sequential decision-making is desirable.
• Familiarity with large language models and their applications in decision-making is a plus.
• Publications in peer-reviewed journals or conference proceedings are considered beneficial.
• Experience working on interdisciplinary research projects is a significant advantage.
• Demonstrated ability to collaborate effectively with faculty and peers is preferred.
• Previous involvement in healthcare systems optimization projects will strengthen the application.
• Strong communication skills for presenting research findings in academic and professional settings are a plus. 

Application Process 

Interested candidates are encouraged to apply for the Ph.D. position through the online application link provided:

• Applicants should submit a current Curriculum Vitae (CV) highlighting academic and research achievements.
• Official academic transcripts from all prior degrees are required for review.
• A research statement outlining the candidate’s interests and potential contributions is also requested.
• Applications will be evaluated on a rolling basis, so early submission is strongly encouraged.
• The position will remain open until a suitable candidate is selected.
• Shortlisted candidates will be contacted for further evaluation, including interviews.
• Discussions will also cover alignment with ongoing research projects in the lab.

For direct application, please visit here.  

Additional information about the lab and current research can be found here.