Tenure Track Assistant Professor in Operations Research and AI

Tenure Track Assistant Professor in Operations Research and AI at Technical University of Munich

The Technical University of Munich (TUM) invites applications for the position of Tenure Track Assistant Professor in Operations Research and AI, to begin as soon as possible. This is a W2 fixed-term (6 year) tenure-track professorship with the possibility for promotion to a tenured W3 position.

Scientific Environment

This professorship will be assigned to the TUM School of Management, located in Munich. The TUM School of Management is recognized as one of the leading management schools in Germany and internationally. Our core focus in research and teaching lies at the intersection of management and technology, where we strive to create significant impact. Our esteemed faculty members regularly publish in top business, economics, and general science journals. The successful candidate will join an interdisciplinary research environment with strong ties to industry.

Responsibilities

Key responsibilities include engaging in cutting-edge research and teaching, as well as fostering the development of early-career scientists. We are specifically seeking an expert in the research area of Operations Research with a strong emphasis on Artificial Intelligence, particularly data-driven optimization and machine learning. Teaching duties will encompass courses across the university’s bachelor's, master's, and PhD programs, with a particular focus on the Master in Management and Technology program.

Qualifications

Candidates should demonstrate initial scientific achievements and a proven capacity for independent research at the highest international level. The ideal candidate will possess strong methodological expertise in both Operations Research and Artificial Intelligence, including their intersections, and show significant potential to publish in leading academic journals. Essential prerequisites include a university degree, an outstanding doctoral degree or equivalent scientific qualification, and demonstrated pedagogical aptitude. Substantial research experience abroad is also expected.

Our Offer

TUM provides a merit-based academic career path for tenure track faculty, structured according to the best international standards and transparent performance criteria. This path progresses from Assistant Professor to a permanent Associate Professor position, and potentially to Full Professor. The regulations of the TUM Faculty Recruitment and Career System apply.

TUM offers excellent working conditions within a vibrant scientific community, situated in the dynamic research environment of the Greater Munich Area. The TUM environment is multicultural, utilizing English as a common language for scientific interaction. TUM provides attractive salary conditions and comprehensive social benefits.

The TUM Munich Dual Career Office (MDCO) offers tailored career consulting for partners of newly appointed professors, assisting with the relocation and integration of new professors, their partners, and accompanying family members.

Your Application

TUM is an equal opportunity employer and actively encourages applications from women. This position is suitable for disabled persons, who will be given preference in cases of generally equivalent suitability, aptitude, and professional performance.

Please submit application documents in accordance with TUM’s guidelines for professors. Detailed information about the TUM Faculty Recruitment and Career System and these guidelines are available at www.tum.de/faculty-recruiting. Information on collecting and processing personal data is also available there.

The application deadline is March 30, 2026. Please submit your application via the TUM recruitment portal: www.recruit.tum.de

Key skills/competency

• Operations Research
• Artificial Intelligence
• Data-driven Optimization
• Machine Learning
• Academic Research
• University Teaching
• Scientific Publication
• Interdisciplinary Collaboration
• Early-career Scientist Mentoring
• Quantitative Methods