Post Doc Research Associate

Req Id: 44388 Job Title: Post Doc Research Associate City: West Lafayette Job Summary Postdoctoral Research Associate Supply Chain Optimization, Inventory Analytics & Emerging Computing Jung Research Group, Purdue University Purdue University – West Lafayette, Indiana The Jung Research Group at Purdue University invites applications for a Postdoctoral Research Associate working at the intersection of supply chain management, inventory optimization, operations research, and data-driven decision making. The Jung Research Group develops quantitative methods across a broad range of scientific and engineering problems, with activities spanning particle physics, artificial intelligence and machine learning, quantum computing, and advanced instrumentation. A recurring theme of the group’s work is the development and rigorous benchmarking of computational methods for complex, high-dimensional problems. More information on the group and its research activities is available at the Jung Research Group website: Jung Research Group at Purdue University . This postdoctoral position is situated at the frontier of supply-chain and inventory-management applications, with strong connections to real-world industrial and defense-related problems through collaborations with external industrial and federal partners. The successful candidate will develop and evaluate advanced operations-research and data-analytics methods using realistic operational datasets, with an emphasis on translating modern optimization, forecasting, and uncertainty-aware methods into practical decision-support tools. A distinctive component of the position will be the opportunity to investigate emerging computing approaches, including quantum and hybrid quantum-classical optimization, alongside state-of-the-art classical methods. Research Scope The postdoctoral researcher will work on problems such as: inventory optimization and inventory-policy design; multi-echelon and multi-location inventory systems; demand forecasting and uncertainty quantification; supply-chain planning, replenishment and allocation; stochastic and robust optimization under uncertain demand, lead times and supply; large-scale combinatorial optimization; predictive and prescriptive analytics; integration of machine learning with optimization; simulation and digital-twin approaches for supply-chain decision making; resilience, disruption response and scenario analysis; development of data-driven decision-support and optimization tools. The project will also explore whether selected industrial problems can benefit from novel computational paradigms, including quantum annealing, gate-based quantum optimization, quantum-inspired methods, and hybrid quantum-classical algorithms. These approaches will be evaluated against rigorous classical benchmarks rather than treated as replacements for established operations-research methodology. Responsibilities The successful candidate will: formulate real-world supply-chain and inventory problems as quantitative optimization and decision models; analyze large operational datasets and identify actionable structure, trends and uncertainties; develop, implement and benchmark optimization algorithms; combine forecasting, machine learning and operations-research methods where appropriate; work with industrial collaborators to translate operational needs into tractable research problems; develop reproducible computational workflows and research-quality software; investigate emerging computational approaches for difficult optimization problems; compare novel algorithms systematically with state-of-the-art classical approaches; publish results in peer-reviewed journals and present work at major conferences; contribute to research proposals and externally funded R&D programs; interact with graduate and undergraduate researchers working on related projects. Required Qualifications Candidates should hold, or expect to receive before the start date, a Ph.D. in Industr…

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