Research at Heinz College (https://www.heinz.cmu.edu/) is driven by a strong passion to understand and improve our society analytically - in areas such as healthcare management, rural health, distributed health delivery, mobility, statistical fairness, privacy, supply chains, organizational behavior, workforce economics, and the future of work. Successful candidates will join an active group of colleagues with expertise spanning operations research, optimization, statistics, information systems, economics, and public policy.
The core research agenda centers on robust optimization theory and its applications to real-world decision-making under uncertainty. Possible topics for the postdoctoral positions include classical robust optimization; distributionally robust optimization and data-driven uncertainty or ambiguity sets; resilient supply chains; rural and distributed health delivery systems; healthcare operations and access; improving mobility; identifying and improving social determinants of health; automation in logistics; AI safety; and understanding the theoretical properties of decision models that naturally arise from these application domains.
The postdoctoral fellows will report primarily to Prof. Peter Zhang and Prof. Holly Wiberg. They will be encouraged to publish in top-tier journals and conferences and may collaborate both within Heinz and across Carnegie Mellon University, including the Tepper School of Business, the Department of Civil and Environmental Engineering, the School of Computer Science, and CMU's broader entrepreneurship ecosystem. Fellows will receive regular mentorship throughout the lifecycle of research projects and will be encouraged to disseminate research findings in major conferences and university seminars. Opportunities may also be available to mentor graduate students and to explore commercialization pathways for research with practical impact.
The appointment is for one year, with an option of renewal for a second year contingent on satisfactory progress. The preferred start date is flexible, with earliest availability in Fall 2026.
Qualifications:
Candidates should have a PhD by Fall 2026 in operations research, industrial engineering, management science, computer science, mathematics, statistics, or a closely related field. Successful candidates should have a strong research record in optimization, broadly defined, with solid mathematical foundations in areas such as linear programming, convex optimization, combinatorial optimization, stochastic optimization, or robust optimization. Experience with distributionally robust optimization, machine learning, data-driven methods, healthcare delivery, supply chains, or other applied decision-making problems is especially welcome. Excellent written and oral communication skills are expected.
