TUM Opens Tenure‑Track Assistant Professorship in Mathematics of Machine Learning

Munich, Germany — The Technical University of Munich (TUM) has announced a tenure‑track assistant professorship in Mathematics of Machine Learning, offering an exciting opportunity for emerging researchers at the intersection of mathematics and artificial intelligence. The position reflects TUM’s commitment to fostering cutting‑edge research and training in areas driving digital transformation and innovation.

This tenure‑track role is aimed at candidates ready to develop an independent research program, contribute to high‑impact scientific advances, and play a key role in teaching at one of Europe’s top technical universities. Early‑career scholars with strong mathematical backgrounds and expertise in machine learning theory will be considered.

Focus: Mathematics of Machine Learning

The successful candidate will conduct research and teach in areas that apply advanced mathematical methods to machine learning and data science. Key research themes include:

• Statistical learning theory and generalization analysis
• Optimization algorithms for complex models
• Probabilistic modeling and inference
• High‑dimensional statistics
• Mathematical foundations of artificial intelligence

The role emphasizes theoretical rigor and aims to build bridges between abstract mathematical frameworks and practical machine learning challenges. Applicants should demonstrate a strong record of research potential and a clear plan for building an independent program in relevant mathematical areas.

Research Leadership and Collaboration

The assistant professor will join TUM’s vibrant research ecosystem, collaborating with faculty members in mathematics, computer science, data science, and engineering. The environment supports interdisciplinary work and encourages partnerships with industry and academic institutions.

TUM particularly values contributions that engage with real‑world applications while advancing foundational research — a combination that strengthens both scientific understanding and technological progress.

Teaching and Academic Duties

Alongside research, the tenure‑track assistant professor will have teaching responsibilities at undergraduate and graduate levels. Courses may include machine learning theory, mathematical foundations of data science, and related advanced topics. Candidates should be prepared to mentor graduate students and contribute to curriculum development in mathematics and information sciences.

Qualifications and Expectations

Applicants are expected to hold a doctoral degree in mathematics, computer science, or a related field, with strong emphasis on mathematical foundations of machine learning. A demonstrated ability to publish in reputable journals, present research internationally, and engage in collaborative projects is essential.

The tenure‑track position provides a clear path toward a permanent professorship based on performance evaluations, research achievements, and teaching excellence.

Diversity and Equal Opportunity

TUM is committed to fostering an inclusive academic community and encourages applications from underrepresented groups, women, and international scholars. The university supports equal opportunity in hiring practices and academic advancement.

Application Timeline

While specific deadlines vary by recruitment cycle, candidates are encouraged to prepare application dossiers — including a curriculum vitae, research and teaching statements, and representative publications — well in advance of spring review periods. Successful applicants will be evaluated competitively during expert committee review phases.

This tenure‑track assistant professorship represents a high‑impact opportunity for early‑career academics who aim to shape the future of mathematical research in machine learning within a dynamic international research environment.