Postdoctoral Researcher in Stochastic Optimization (Transportation, Remote)

Job summary

This position focuses on developing advanced stochastic optimization methods to facilitate the transition from diesel to battery-electric freight transport. The role involves collaboration with academic and industrial partners to address complex fleet-planning challenges under uncertainty.

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.
• Proficient in scientific programming with Python, Julia, C++, or similar languages.
• Ability to write and publish research 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 mathematical models for fleet replacement, charging investments, and vehicle technology choices under uncertainty.
• Design and implement stochastic programming and simulation-based computational methods.
• Quantify trade-offs between cost, emissions, and service risks across uncertain transition scenarios.
• Analyze the impact of charging infrastructure on logistics networks.
• Translate optimization results into clear decision-making guidelines for fleet operators and policymakers.
• Publish research findings in international peer-reviewed journals.
• Present research at international conferences and contribute to project reporting.

Skills

• Strong analytical and problem-solving skills.
• Excellent communication skills for presenting complex concepts clearly.

Education

• A PhD degree (or equivalent) is required.

Tools

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