PhD student in machine learning for brain health

The “Hertie Institute for AI in Brain Health” (Hertie AI) at the Faculty of Medicine of the Eberhard Karls University Tübingen is funded by the Gemeinnützige Hertie Stiftung to develop AI algorithms, which detect diseases of the nervous system earlier and treat them better.

Tasks

• Design and implement novel machine learning algorithms (e.g. equivariant architectures, self-supervised learning, or optimal transport) for brain health data
• Apply these methods to biomedical imaging, neuronal time series, or multimodal single-cell data
• Improve clinical workflows and uncover new insights into the cellular landscape of the brain
• Publish and present research results at international venues
• Contribute to project management, teaching, and grant preparation.

Profile

• Very good Master’s degree in Computer Science, Bioinformatics, Computational Neuroscience, Cognitive Science or a related field
• Strong interest and experience in machine learning algorithm development and open- source software (Python)
• Proficiency in handling complex biomedical imaging, neuronal time series or single-cell datasets
• Curiosity, excellent analytical and communication skills
• Ability to work effectively in an interdisciplinary team
• Prior publications at conferences or in journals are a plus

Benefits

• Exciting and supportive environment with access to state-of-the-art compute facilities, interesting and diverse medical data sets and 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)

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