Tenure Track Assistant Professor in Mathematics of Machine Learning Technical University of Mun[...]

Euro Math Soc; München; Vor Ort; EUR 90.000 - 120.000; Vollzeit

Benefits dieser Stelle

Merit-based academic career path

Attractive salary conditions

Support for relocation and integration

Zusammenfassung

A leading technical university in Munich is seeking a Tenure Track Assistant Professor in Mathematics of Machine Learning. This role includes responsibilities in research and teaching, with a focus on mathematical methodologies in machine learning. Candidates should have a strong academic background, substantial research experience, and the ability to develop new methodologies. The position offers an opportunity to contribute to TUM's AI initiatives and work in a vibrant scientific community.

Qualifikationen

• Demonstrated initial scientific achievements and capacity for independent research.
• Substantial research experience abroad.
• Pedagogical aptitude.

Aufgaben

• Research and teach in the field of Mathematics of Machine Learning.
• Integrate developments in machine learning into research and teaching.
• Promote early-career scientists.

Kenntnisse

Mathematical Analysis

Optimization

Probability Theory

Deep Learning

Generative Models

Robust Learning Methods

Ausbildung

Outstanding doctoral degree or equivalent scientific qualification

University degree

Jobbeschreibung

The Technical University of Munich (TUM) invites applications for the position ofTenure Track Assistant Professorin » 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 EnvironmentThe professorship will be assigned to the Department of Mathematics in the TUM School of Computation, Information and Technology.ResponsibilitiesThe 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)• Optimization• Probability 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.QualificationsWe 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 (please see www.tum.de/en/faculty-recruiting-faq/ for further information).Our OfferBased 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.Your ApplicationTUM is an equal opportunity employer and explicitly encourages applications from women. The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.Application documents should be submitted in accordance with TUM’s application guidelines for professors. These guidelines and detailed information about the TUM Faculty Recruitment and Career System are available at www.tum.de/faculty-recruiting. Here you will also find TUM’s information on collecting and processing personal data as part of the application process.Please submit your application by 26 February 2026 via the TUM recruitment portal: www.recruit.tum.deLast updated: 21 January 2026