Talayeh Razzaghi

The School of Industrial and Systems Engineering at the University of Oklahoma is inviting applications for a funded PhD position under the supervision of Associate Professor Talayeh Razzaghi. The research project focuses on the intersection of optimization, machine learning, and healthcare analytics, aiming to develop advanced data-driven methods to enhance decision-making and improve patient outcomes in healthcare settings. Applicants should hold or be nearing completion of an MS degree (within the last 0–2 years) in Operations Research, Industrial Engineering, or Mathematics. A strong foundation in optimization methods, such as nonlinear and discrete optimization, is essential. Proficiency in programming languages like Python or R is required, and experience with machine learning is preferred but not mandatory. The ideal candidate will be passionate about interdisciplinary research that bridges data science, optimization, and healthcare applications, and will possess strong communication skills and a collaborative mindset. This opportunity provides funding, though specific details regarding the stipend or tuition coverage are not disclosed. The position offers the chance to work on real-world healthcare challenges and contribute to impactful research in a supportive academic environment. To apply, interested candidates should send their CV and a brief statement of interest to [email protected]. For more information, you may refer to the LinkedIn profile of Professor Talayeh Razzaghi or the related LinkedIn post.

Ph.D. opportunity in Industrial Engineering at the University of Oklahoma with a funded research project starting in Fall 2026 or Spring 2027 . The project sits at the intersection of optimization , machine learning , and healthcare analytics , with the goal of developing data-driven methods that support decision-making and improve patient outcomes. This is a strong fit for applicants interested in operations research , data science , and applied research in healthcare settings. Eligibility highlights: a recent or soon-to-be-completed M.S. degree (preferably within the last 0–2 years) in Operations Research, Industrial Engineering, Mathematics, or a closely related field; strong background in nonlinear and discrete optimization; proficiency in Python, R, or similar programming environments; machine learning experience preferred but not required; strong communication and teamwork skills. Funding: the position is described as funded. No stipend amount, tuition details, or application deadline were provided. How to apply: interested applicants should email their CV and a brief statement of interest to [email protected].