The Probabilistic Machine Learning Group at Saarland University (Germany) is seeking for several PhD students (funded initially for 3 years) and postdoctoral researchers (funded initially for 2 years) to work on (probabilistic) machine learning and its societal impact.
– About us –
We develop cutting-edge trustworthy machine learning methods to be deployed in the real-world. Our research can be broadly categorized in three main topics: fair, interpretable and robust machine learning. We are an active and diverse research team, with interests in a wide range of ML approaches including deep learning, probabilistic modeling, causal inference, time series analysis, and many more. Our research has a strong societal component and can be applied in a broad range of application domains, from medicine and psychiatry to social and communication systems. Examples of ongoing research projects can be found here.
– Requirements –
PhD applicants should have a MSc (or in exceptional cases, a BSc) degree in CS, math or a related field, and demonstrate a strong background in machine learning, math/statistics and programming (ideally in Python using PyTorch).
Postdoctoral applicants should have or be about to complete a PhD in computer science or a related field and have demonstrated excellent research skills through a strong publication record in top ML venues, such as NeurIPS, ICML, ICLR, AISTATS, JMLR, etc.
All applicants should demonstrate sufficient spoken and written communications skills in English.
– How to Apply –
To apply follow the instructions in our website (and/or via the ELLIS PhD Program, deadline November 15).
Isabel Valera
Full Professor at the Department of Computer Science of Saarland University, Saarbrücken (Germany)
Adjunct Faculty at Max Planck Institute for Software Systems, Saarbrücken
https://machinelearning.uni-saarland.de/