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PhD Position in Health Systems Optimization

Kennesaw State University Department of Industrial and Systems Engineering
✓ Fully Funded 🎓 Data Science machine learning simulation mathematical modeling health systems optimization markov modeling resource allocation healthcare logistics data-driven optimization

Explore advanced optimization and analytics to solve real-world healthcare challenges. Join a supportive lab under Dr. Maryam Eghbalizarch at Kennesaw State University focused on improving health systems and patient outcomes.

AI-generated overview

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Why This Research Matters

This research advances healthcare by providing data-driven and optimized solutions to improve patient flow, resource allocation, and operational efficiency. Improving healthcare systems directly benefits patient outcomes and reduces costs, which is critical for the evolving demands in healthcare delivery.

Operations research Data science Markov decision process Healthcare systems Supply chain

Project Description

Project Overview

The Health Systems Optimization Lab at Kennesaw State University specializes in advancing healthcare delivery through mathematical modeling, simulation, and data-driven optimization. The PhD research will target challenges in healthcare systems such as resource allocation, patient flow, and operational efficiency using interdisciplinary analytic and computational techniques.

What You Will Do

The candidate will work under the supervision of Dr. Maryam Eghbalizarch within the HSOpt Lab. Research activities include applying optimization methods, Markov modeling, simulation, and machine learning to real-world healthcare problems. Collaboration with healthcare professionals and industry partners will provide practical insights and application-driven research opportunities.

Expected Outcomes

This research will develop novel solutions to improve patient outcomes, reduce costs, and increase the operational efficiency of healthcare systems. The integration of data science and artificial intelligence will contribute to more accessible and higher quality healthcare delivery models.

Why This Matters

Optimizing health systems is critical in today’s rapidly changing healthcare environment. This work addresses real healthcare challenges with advanced analytics, directly impacting care quality, cost management, and resource efficiency, ultimately benefiting patients and providers.

Entry Requirements

Master’s degree in Industrial Engineering, Operations Research, or a closely related field. Strong background in data-driven optimization, Markov modeling, simulation, and programming in Python, R, or Julia. Demonstrated academic excellence and motivation.

How to Apply

Submit Curriculum Vitae, academic transcripts, and research statement via https://forms.gle/SnNPDzNnx5nGVbai9. Applications reviewed on a rolling basis until the position is filled.

Eligibility

UK/Home
EU
International

Supervisor Profile

DM
Dr. Maryam Eghbalizarch
Kennesaw State University, Department of Industrial and Systems Engineering

Dr. Maryam Eghbalizarch is an Assistant Professor in the Department of Industrial and Systems Engineering at Kennesaw State University. She leads research within the Health Systems Optimization Lab, focusing on applying optimization, Markov modeling, and machine learning to healthcare systems. Her work is interdisciplinary, combining engineering techniques with healthcare applications to enhance care quality and system efficiency.

Key Publications

2018 79 citations
Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment
2022 69 citations
Prioritizing the effective strategies for construction and demolition waste management using fuzzy IDOCRIW and WASPAS methods
2024 49 citations
Evaluation of business strategies based on the financial performance of the corporation and investors' behavior using D-CRITIC and fuzzy MULTI-MOORA techniques: A real case study
2021 34 citations
Solving a new robust reverse job shop scheduling problem by meta-heuristic algorithms
2023 33 citations
A novel fuzzy SECA model based on fuzzy standard deviation and correlation coefficients for resilient-sustainable supplier selection

Research Contributions

Developed methods for pharmacological therapy selection for type 2 diabetes using fuzzy decision-making approaches.
This work aids in more tailored and effective treatment strategies in healthcare systems managing diabetes.
Introduced fuzzy multi-criteria decision-making models for waste management and supplier selection.
These models support sustainable and resilient supply chain management and environmental practices.
Applied meta-heuristic algorithms to solve complex scheduling and scheduling optimization problems.
Improves efficiency and robustness in industrial production and manufacturing scheduling.
Modeled adverse drug reactions in medication treatment of type 2 diabetes using Markov decision processes.
Enhances understanding and management of medication risks in chronic disease treatment.

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