Tenure Track Assistant Professor in Mathematics of Machine Learning

Tenure Track Assistant Professor in » Mathematics of Machine Learning «

19.01., Professuren

The Technical University of Munich (TUM) invites applications for the position of Tenure Track Assistant Professor in » Mathematics of Machine Learning « to begin as soon as possible. The position is a W2 fixed-term (6 year) tenure-track professorship with the possibility for promotion to a tenured W3 position.

Scientific

Environment

The professorship will be assigned to the Department

of Mathematics in the TUM School of Computation, Information and Technology.

Responsibilities

The responsibilities include research and teaching as

well as the promotion of early-career scientists. The professorship “Mathematics

of Machine Learning” offers an exciting opportunity to shape the future of

mathematical research in machine learning at the Technical University of Munich

(TUM). The successful candidate will play a key role in integrating

cutting-edge developments—such as deep learning, generative models, and robust

learning methods—into the research and teaching activities of the TUM CIT

Department of Mathematics, further strengthening the central role of

mathematics in the foundations of artificial intelligence.

We seek to appoint an internationally competitive

researcher with an outstanding academic track record and deep expertise in one

of the following fields :

• Mathematical

Analysis (e.g., functional analysis, harmonic analysis, approximation theory)

Theory and Mathematical Statistics.

We particularly value research that bridges these

areas in a non-trivial and innovative way—ideally leading to significant

advances in the understanding of the expressiveness, training dynamics,

stability, and generalization behavior of machine learning models. A strong

ability to develop new machine learning methodologies and to rigorously analyze

them using mathematical tools is essential.

The successful candidate is also expected to be

closely involved in TUM’s strategic AI initiatives and research infrastructure.

Relevant institutional partners include the Munich Data Science Institute

(MDSI), the Munich Institute of Robotics and Machine Intelligence (MIRMI), the

Munich Center for Machine Learning (MCML), the Munich ELLIS Unit, and the

Konrad Zuse School of Excellence in Reliable AI. These collaborations offer

unique opportunities for interdisciplinary research and impact.

The professorship includes

teaching responsibilities in the international Master’s program Mathematics in

Data Science, as well as in related core areas of mathematics such as analysis,

optimization, probability theory, and statistics. A suitable contribution to

the teaching of mathematics in the Bachelor’s programs and for other TUM

Schools is expected.

Qualifications

We are looking for candidates who have demonstrated

initial scientific achievements and the capacity for independent research at

the highest international level. A university degree and an outstanding

doctoral degree or equivalent scientific qualification, as well as pedagogical

aptitude, are prerequisites. Substantial research experience abroad is expected

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for further information).

Our Offer

Based on the best international standards and

transparent performance criteria, TUM offers a merit-based academic career

path for tenure track faculty from Assistant Professor through a permanent

position as Associate Professor, and on to Full Professor. The regulations of

the TUM Faculty Recruitment and Career System apply.

TUM provides excellent working conditions in a lively

scientific community, embedded in the vibrant research environment of the

Greater Munich Area. The TUM environment is multicultural, with English serving

as a common interface for scientific interaction.TUM offers attractive and performance-based salary conditions and

social benefits].

The TUM Munich Dual Career Office (MDCO) provides

tailored career consulting to the partners of newly appointed professors. The MDCO

assists the relocation and integration of new professors, their partners and

accompanying family members.