PhD Position (m/f/d) in Machine Learning for Multiobjective Combinatorial Optimization

13.03.2026, Wissenschaftliches Personal

The PhD project investigates how reinforcement learning methods can be used to solve multiobjective discrete and combinatorial optimization problems. The goal is to develop new algorithmic approaches that combine ideas from machine learning, optimization, and algorithm design.

Your tasks:

• Design and analyze new reinforcement learning algorithms for solving multiobjective combinatorial optimization problems.
• Implement and experimentally evaluate the developed methods using modern ML frameworks (e.g., PyTorch).
• Benchmark the developed approaches on standard combinatorial optimization problems.
• Present research results at international machine learning and optimization conferences and publish them in scientific journals.
• Teach tutorials (in English) for the courses Advanced Mathematics 1–2 and/or Statistics at TUM Campus Straubing.

Your profile:

• Above-average master’s degree in mathematics, theoretical computer science, machine learning, or a closely related field.
• Strong background in discrete optimization, algorithms, or reinforcement learning.
• Good programming skills (preferably Python) and experience with machine learning frameworks such as PyTorch or TensorFlow.
• Strong analytical and problem-solving skills and interest in mathematical research.
• Experience with multiobjective optimization is a plus.
• Very good command of spoken and written English.

We offer:

• A stimulating international research environment at the interface of mathematics and computer science.
• Close supervision and support for developing an independent research profile.
• Opportunities to publish in leading journals and present at international conferences.
• Funding for conference travel and research visits.
• Flexible working hours and a friendly working atmosphere.
• A 3-year contract (75% TV-L E13 during the first 8 months, increasing to 100% afterwards) with salary and benefits according to the public service agreement of Bavaria.

How to apply?

Interested candidates should submit their application as a single PDF file via email to Prof. Dr. Clemens Thielen: clemens.thielen@tum.de

The application should include:

• Letter of motivation explaining your interest and suitability for the position.
• Curriculum vitae (CV).
• Transcripts and degree certificates of your BSc and MSc studies (with detailed course and grade information).
• If applicable: List of relevant projects and publications.
• Optional: A letter of recommendation from your MSc thesis supervisor.

Applications will be reviewed starting 30 April 2026 and will remain open until the position is filled.

Kontakt: clemens.thielen@tum.de