Postdoctoral Researcher in Stochastic Optimization

Job summary

This position focuses on advancing stochastic optimization methods for the transition to battery-electric freight transport, addressing a critical decarbonization challenge. The role is part of the TEDDY project at a leading technical university, working under the guidance of prominent researchers in the field.

Qualifications

• PhD in Operations Research, Applied Mathematics, Computer Science, Transportation Modelling, or a related field.
• Strong background in mathematical optimization, particularly in mixed-integer programming, stochastic programming, or decomposition methods.
• Proficiency in scientific programming languages such as Python, Julia, or C++.
• Ability to publish research findings in English.
• Experience with Sample Average Approximation, robust optimization, Monte Carlo methods, or large-scale MIP solvers is advantageous.
• Interest in freight transport, logistics, or electric vehicles is a plus.

Responsibilities

• Develop stochastic and probabilistic optimization models for fleet operators and policymakers.
• Create mathematical models for fleet replacement, charging investments, and vehicle technology choices under uncertainty.
• Design and implement computational methods based on stochastic programming and simulations.
• Analyze cost, emission, and service-risk trade-offs across various transition scenarios.
• Assess the impact of charging infrastructure on logistics networks.
• Translate optimization results into clear decision-making guidelines.
• Publish research in international peer-reviewed journals and present findings at conferences.

Education

• A PhD degree (or equivalent) is required.

Tools

• Proficiency in programming languages such as Python, Julia, or C++.