• Uncovering generative and causal mechanisms in neural recordings to identify novel biomarkers
• Advancing interpretability in AI models that infer dynamical systems from time series data
• Developing multi-modal control-theoretic AI approaches for causal intervention We value individuals who thrive in interdisciplinary environments and are passionate about impactful research at the intersection of machine learning and healthcare

• Strong background in machine learning, dynamical systems, and optimal control to tackle real-world challenges in medicine and neuroscience
• Experience with neuroimaging data such as fMRI time series or other neural recordings
• Solid grasp of modern machine learning techniques, including generative models for learning dynamical systems and model-predictive control methods
• Enthusiastic about working with diverse data modalities and driven by a desire to integrate these modalities to advance diagnostics and treatments in neurology and psychiatry

Benefits

• Exciting and supportive environment with access to state-of-the-art compute facilities
• Interesting and diverse medical data sets
• Mentoring and career advice through experienced faculty
• Hertie AI closely collaborates with the world-class AI and neuroscience ecosystem in Tübingen (e.g. Cyber Valley, Cluster of Excellence “Machine Learning in Science”, Tübingen AI Center, Hertie Institute for Clinical Brain Research)

The Faculty of Medicine is one of the four founding faculties of the Eberhard Karls University of Tübingen. With its non-clinical facilities as well as its research and teaching area corresponding to the organisational units of the University Hospital, it is one of the largest medical training and research institutions in Baden-Württemberg.