2 Research Associates / PhD Students Machine Learning and Combinatorial Optimization)

Do you have experience in Machine learning?, Do you have a Doctoral degree?, * 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 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. At the Faculty of Computer Science, Institute of Artificial Intelligence, the Chair of Machine Learning for Computer Vision offers two full-time positions as Research Associate / PhD Student (m/f/x) Machine Learning and Combinatorial Optimization (subject to personal qualification employees are remunerated according to salary group E 13 TV-L) starting at the earliest possible date. The positions are limited to three years. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz - WissZeitVG). The positions offer the chance to obtain further academic qualification (usually PhD). Tasks: * 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