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 Industrial Engineering, Operations Research, Management Science, Applied Mathematics, Systems Engineering, or a closely related quantitative discipline.

Strong candidates will have demonstrated expertise in several of the following:

• operations research and mathematical optimization;
• inventory theory and/or supply-chain optimization;
• stochastic optimization, dynamic programming, robust optimization, or simulation;
• statistical data analysis and quantitative modeling;
• Python and modern scientific/data-analysis workflows;
• Proven record of experience in analysis of large data sets and statistical inference methods, and especially of handling and analyzing large, imperfect real-world datasets;
• clear scientific communication and independent research.

Particularly Desirable Experience

Experience in one or more of the following would be advantageous:

• multi-echelon inventory optimization;
• demand forecasting and time-series analysis;
• reinforcement learning or approximate dynamic programming;
• machine learning for operations and supply-chain applications;
• digital twins or discrete-event simulation;
• prescriptive analytics and decision-support systems;
• SQL, database systems and industrial data pipelines;
• logistics, scheduling, routing or network optimization;
• uncertainty quantification and scenario generation;
• implementation of algorithms in operational or industrial environments.

Prior quantum-computing experience is not required. However, candidates should have a strong interest in learning and critically evaluating emerging computational approaches. Experience with quantum optimization, quantum annealing, QUBO formulations, QAOA, or hybrid quantum-classical algorithms would be a plus.

Research Environment

The position offers an opportunity to work across the boundary between fundamental methodological research and industrial-scale application in the Jung research group (https://www.physics.purdue.edu/jung/index.html). Projects will be motivated by real operational problems and datasets, while providing the freedom to investigate new mathematical, computational and algorithmic approaches.

The researcher will interact with faculty, graduate students, industrial collaborators and specialists in optimization, data science, AI/ML and emerging computing technologies across Purdue and with advanced computing platforms (D-Wave, IBM-Q, etc.).

Application

Applicants should submit:

• a cover letter describing their research background and interest in the position;
• curriculum vitae;
• a brief statement of research interests;
• names and contact information for three references; and
• up to three representative publications.

Review of applications will begin immediately and continue until the position is filled.

Purdue University is an equal opportunity/equal access university.