PhD Positions on Machine Learning for Process Mining (ML4ProM)
The federal state government of North Rhine-Westphalia created a new initiative to strengthen the research in AI and ML. The project Machine Learning for Process Mining (ML4ProM) will have two PhD working on the interplay between process mining and machine learning, combining the expertise of RWTH Aachen University & Universität Bielefeld. These PhDs will be part of a larger ML/AI network called Data-NInJa (around 15 PhDs in total).
The scope of the PADS group at RWTH includes all topics where discrete processes are analyzed, reengineered, and/or supported in a data-driven manner. The main focus is on Process Mining (including process discovery, conformance checking, performance analysis, predictive analytics, operational support, and process improvement). This project aims to combine this with the ML expertise of Prof. Dr. Barbara Hammer in Bielefeld. The PhD focusing on process mining will be hosted by the Process and Data Science Group (PADS) group.
The PADS group works on the whole spectrum ranging from theoretical research to applied research. RWTH Aachen University is one of the leading technical universities in Germany (typically ranked in top-3). It is one of the so-called Universities of Excellence, and the Department of Computer Science at RWTH is one of the strongest in Europe with over 30 professors.
Appointments are 100% and based on the federal wage agreement (Tarifvertrag der Länder or TV-L, see http://oeffentlicher-dienst.info/tv-l/. Note that the salaries are very high compared to most other countries, but applicants are expected to be self-propelling, autonomous, and function as employees (rather than students).
Please send your application (including a motivation letter and full CV) via e-mail to email@example.com, and you will be asked to fill out a form. Note that there also several other PhD and Postdoc positions in the PADS group. If you have questions, please also use the e-mail address firstname.lastname@example.org or call +49 241 80 21902.