43 postdoctoral-deep-learning "https:" PhD positions at Technical University of Munich
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Learning Matters!. Task You will break with the current focus on the brain to uncover the physics of continual learning instead by investigating the emergence of learning bottom-up in life, reduced in
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–2 years, total 3–4 years) on deep learning for medical imaging. This DFG-funded project focuses on developing deep learning methods for medical and scientific imaging. The Professorship for Machine
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foundations of medical deep learning. The project focuses on novel self-supervised objectives, information geometry, mitigating representation bias for rare pathological findings, and building next-generation
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31.07.2026, Academic staff We are seeking a researcher in Scientific Machine Learning (SciML) to join the project "Data science at scale" at the Technical University of Munich, Germany. Ideal
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and experience with modern deep learning frameworks (e.g. PyTorch) Solid background in machine learning, ideally with experience in NLP, large language models, or sequence modeling Interest in clinical
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learn new techniques and work across disciplines is essential, as is a focus on mechanistic understanding, reaction kinetics, and analytical methods. Candidates should have very good English communication
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-style models. Pretraining may use self-supervised, contrastive, masked-modelling, or generative objectives. Own research ideas are strongly encouraged. Your responsibilities • Develop deep learning
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Solid mathematical foundations and strong programming skills Proficiency in Python and experience with deep learning frameworks such as PyTorch Ability to work independently and take initiative in driving
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programming skills in Python; initial experience with machine learning frameworks such as PyTorch or TensorFlow Initial practical experience from a master's thesis, study projects, internships, or open-source
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(DCs) master challenging roles, take on substantial responsibility and acquire important transferable skills. 2. To conduct studies with the proteome atlas of the 100 most important crop plants for human