2024 Call for PhD Applications

On the 11th and 12th of October, relAI welcomed the new cohort of relAI Master and Doctoral students. The event included informative sessions about relAI and networking activities. At the Munich Data Science Institute (MDSI), TUM, the relAI directors and coordinators presented the relAI program to the new students. The first relAI cohort of students organised a lively interactive session (photo) to welcome and get to know the new students.

Congratulations to the relAI PhD Student Lisa Wimmer, the relAI fellow Bernd Bischl, and the relAI director Stephan Günnemann on the best paper award of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2023).

ECML PKDD is Europe’s top machine learning and data mining conference, with over 20 years of successful events and conferences across the continent. The ECML PKDD 2023 was held in Turin, Italy from the 18th to the 22nd of September 2023.

List of authors and title of the awarded paper:Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer    
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry

We are excited to announce that applications for the relAI PhD program are now open. Interested candidates can apply through our website. Deadline for applications is January 9th, 2023.

The novel, innovative PhD relAI program offers a cross-sectional training for successful education in AI including scientific knowledge, professional development courses and industrial exposure, providing a coherent, yet flexible and personalised training.

Funded applicants will be hired for three years, including social benefits (TV-L E13 of the German public sector). They are further supported by travel grants, e.g. for conference attendance or research stays. Doctoral students enrol at TUM or LMU depending on the hosting relAI fellow.

We encourage candidates with an excellent master’s degree (or equivalent) in computer science, mathematics, engineering, natural sciences or other data science/machine learning/AI related disciplines and a genuine interest to work on a topic of reliable AI to apply.