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TU Darmstadt

PhD Position in Mathematical Foundations of Deep Learning at TU Darmstadt TU Darmstadt in Germany

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Oct 22, 2026

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Country

Germany

University

TU Darmstadt

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Keywords

Computer Science
Deep Learning
Mathematics
Probability Theory
Linear Algebra
Artificial Neural Network
Random Matrix Theory
Optimisation
Statistics

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About this position

PhD position in Mathematical Foundations of Deep Learning at TU Darmstadt (Germany), within the Signal Theory and Statistical Learning group and the Emmy Noether project RMT4DL: Random Matrix Theory for Deep Learning.

The project focuses on the mathematical foundations of deep learning, especially probability theory, random matrix theory, machine learning theory, optimization, and linear algebra. The research aims to develop theoretical tools for understanding stability, generalization, and feature learning in large-scale neural networks.

This is a PhD opening for a highly motivated candidate with an excellent Master’s degree (or equivalent) in mathematics, computer science, or a closely related field. Strong analytical skills, independent research ability, excellent English, and Python programming experience are expected; familiarity with PyTorch or JAX is a plus.

Location: Darmstadt, Hesse, Germany. Application deadline: 22 October 2026. Further details and the application portal are available via the TU Darmstadt job page.

Contact: Mariia Seleznova ([email protected]).

Funding details

Available

What's required

Applicants should have an excellent Master’s degree or equivalent in mathematics, computer science, or a closely related field. A strong background in machine learning theory, probability theory, optimization, and linear algebra is expected; experience with random matrix theory is a plus. Programming experience in Python is required, and familiarity with deep learning frameworks such as PyTorch or JAX is a plus. Excellent written and spoken English and strong analytical skills are required.

How to apply

Apply through the TU Darmstadt career portal using the provided job page. Review the full vacancy details and submit your application before the deadline. For questions, contact Mariia Seleznova by email.

More information can be found here

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