TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. For TUD diversity is an essential feature and a quality criterion of an excellent university. Accordingly, we welcome all applicants who would like to commit themselves, their achievements and productivity to the success of the whole institution.

— independent, curiosity-driven basic scientific research on fundamental mathematical optimization problems in the fields of machine learning and image analysis
— design and analysis of algorithms for the exact or approximate solution of these problems
— Implementation, empirical analysis, and comparison of these algorithms using data
— publication of research results at leading conferences and in leading journals
— tasks in teaching and academic self-administration

— excellent university degree in mathematics, computer science, or physics
— extensive prior knowledge in discrete mathematics and in one area of mathematical optimization (e.g., discrete optimization, convex optimization)
— strong interest in basic research using rigorous mathematical methods
— very good programming skills in C/C++
— very good command of written and spoken English

— excellent supervision in an outstanding scientific environment
— unique opportunities for collaboration with local, national, and international partners
— IT equipment tailored to individual needs and access to excellent HPC infrastructure
— a modern working environment in a city of science and culture, surrounded by a unique landscape

TUD strives to employ more women in academia and research. We therefore expressly encourage women to apply. The University is a certified family-friendly university. We welcome applications from candidates with disabilities. If multiple candidates prove to be equally qualified, those with disabilities or with equivalent status pursuant to the German Social Code IX (SGB IX) will receive priority for employment.
Please submit your detailed application with the usual documents by February 26, 2026 (stamped arrival date of the university central mail service or the time stamp on the email server of TUD applies), preferably via the TUD SecureMail Portal https://securemail.tu-dresden.de by sending it as a single pdf file to mlcv@tu-dresden.de or to:
TU Dresden, Chair of Machine Learning for Computer Vision, Prof. Dr. Björn Andres, Helmholtzstr. 10, 01069 Dresden, Germany.
Please submit copies only, as your application will not be returned to you. Expenses incurred in attending interviews cannot be reimbursed.

TUD is a founding partner in the DRESDEN-concept alliance.
Reference to data protection: Your data protection rights, the purpose for which your data will be processed, as well as further information about data protection is available to you on the website: https://tu-dresden.de/karriere/datenschutzhinweis.