PhD Position (m/f/d) in Foundations of Machine Learning

With around 15,000 students and 3,800 employees, the Technische Universität Braunschweig is one of Germany’s leading institutes of technology. It stands for strategic and performance-oriented thinking and acting, relevant research, committed teaching, and the successful transfer of knowledge and technologies to the economy and society. We consistently advocate for family friendliness and equal opportunities.

Our research focuses are mobility, engineering for health, metrology, and city of the future. Strong engineering and natural sciences are our core disciplines. These are closely interconnected with economics, social and educational sciences and humanities.

Our campus is located in the midst of one of the most research-intensive regions in Europe. We work successfully together with over 20 research institutions in our neighborhood as we do with our international partner universities.

The newly founded Institute for Statistics and Foundations of Machine Learning (Prof. Nicole Mücke) and the Institute for Applied Mechanics - Division Data-driven modeling of mechanical systems (Prof. Henning Wessels) at TU Braunschweig are looking for applicants for a PhD position in the field of Statistical Learning Theory/Foundations of Machine Learning to be filled as soon as possible.

PhD Position in Foundations of Machine Learning (EG 13 TV-L, 75%)

The position is offered for a period of three years and provides the opportunity to pursue a doctoral degree.

The research area of Statistical Learning Theory investigates the mathematical foundations of learning from data. A central focus lies in the development and theoretical analysis of modern learning algorithms with provable statistical performance guarantees, in particular under uncertainty, finite sample sizes, and noisy or high-dimensional data. Core research questions include generalization, stability, regularization, and optimal convergence rates of learning algorithms (e.g. kernel methods or neural networks), as well as the quantification and calibration of statistical uncertainty.

The PhD project is situated at the interface of applied mechanics, data-driven modeling, and statistical learning theory. Applied mechanics provides the physical and mathematical description of complex mechanical systems, such as deformable materials governed by nonlinear partial differential equations. Data-driven modeling aims to complement and extend these classical models by learning system behavior from data while respecting underlying physical principles. The project focuses on developing and analyzing learning-based methods for mechanical systems, with an emphasis on theoretical understanding, generalization, and reliability of data-driven approaches.

Your Responsibilities:

• You will conduct research on the mathematical foundations of modern data-driven learning methods, for example physics informed machine learning/neural networks. The doctoral project combines theoretical results from statistical learning theory with data-analytic methods and contributes to the development of mathematically well-founded and reliable models.
• You will contribute to university teaching, including the preparation and delivery of tutorials, supervision of student projects, and grading of examinations and other coursework.
• You will support and carry out research-related academic activities, such as the compilation of scientific materials, publication of research results, and participation in national and international conferences.

Required Qualifications:

• You hold a completed university degree (Master’s degree or equivalent) in Mathematics (ideally with a focus on Statistics, PDEs or Machine Learning).
• You have good command of both German and English.

Desirable Qualifications:

• Solid background in probability theory, statistics, optimization, or statistical learning theory.
• Experience with data-driven methods such as kernel methods or neural networks.
• Interest in theoretical aspects of machine learning, including generalization, stability, regularization, or uncertainty quantification.
• Programming experience in at least one scientific programming language (e.g. Python, Julia).
• Motivation to work on mathematically rigorous research questions in an interdisciplinary and application-oriented research environment.

Our Benefits:

• The opportunity to work on an exciting, forward-looking research topic in an inspiring academic environment as part of the university community.
• A vibrant campus life in an international atmosphere, with numerous intercultural activities and international collaborations.
• Salary in accordance with the German public sector pay scale TV-L • Flexible working hours and part-time options, as well as a family-friendly university culture, certified since 2007 by the “Family-Friendly University” audit.
• Interdisciplinary and cross-faculty support on the path to the doctoral degree through the Graduate Academy GradTUBS.

Further notes

We welcome applicants of all nationalities. At the same time, we encourage people with severe disabilities to apply. Applications from severely disabled persons will be given preference if they are equally qualified. Please attach a proof of disability to your application. We are also working on the fulfilment of the Central Equality Plan based on the Lower Saxony Equal Rights Act (Niedersächsisches Gleichberechtigungsgesetz—NGG) and strive to reduce under-representation in all areas and positions as defined by the NGG. Therefore, applications from women are particularly welcome in this case.

The personal data will be stored for the purpose of processing the application. By submitting your application, you agree that your data may be stored and processed electronically for application purposes in compliance with the provisions of data protection law. Further information on data protection can be found in our data protection regulations at www.tu-braunschweig.de/datenschutzerklaerung-bewerbungen. Application costs cannot be reimbursed.

Questions, Applications

For more information, please contact Prof. Nicole Mücke nicole.muecke(at)tu-braunschweig.de .

Please prepare your application in German or English as a single PDF document consisting of the following:

• Cover letter (including a brief description of your motivation),
• Curriculum vitae,
• Academic records (grades from your previous studies, including information on the grading scale).

The deadline for the application is March 30th, 2026

Please send your application, preferably by email, to nicole.muecke(at)tu-braunschweig.de.

vom: 12.02.2026
gültig bis: 30.03.2026