PhD Position in Machine Learning for Multiobjective Combinatorial Optimization

• 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., * 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.

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• 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.

• 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.