26 development-"https:"-"https:"-"https:"-"CNRS-"-"AIix-Marseille-University" Postdoctoral positions at Technical University of Munich in Germany
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quantitative verification interested in co-developing Automata Tutor - main developer of Automata Tutor Positions in the Formal Methods for Software Reliability group of TU Munich led by Prof. Jan Kretinsky
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talent development. The Terrestrial Ecology Research Group is part of the Department of Life Science Systems in the TUM School of Life Sciences and is based in Freising, Germany. We do fundamental research
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Scientific Personnel 05.03.2026, Academic staff BioSysteM Cluster of Excellence at TUM invites applications for a Postdoctoral Researcher (m/f/d) to lead and further develop the Protein Design
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Chair of Biological Imaging 27.02.2026, Academic staff We are now seeking a highly qualified and motivated postdoctoral fellow (f/m/d) to drive the development of novel optical technologies
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personnel for technical support, ensuring proficient operation. BioSysteM at TUM invites applications for a Postdoctoral Researcher (m/f/d) to lead and further develop the Bioanalytics Facility within
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20.02.2026, Academic staff Full-Time | TV-L E13 | ERC-funded for to 5 years | Focus on developing prompt-based next-generation AI systems for radiotherapy target volume definition Join an ERC-funded
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Chair of Biological Imaging 02.02.2026, Academic staff We now seek a highly qualified and motivated Post-doctoral researcher (f/m/x) to drive the development of a novel quantum enhanced microscope
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Chair of Biological Imaging 10.02.2026, Academic staff We now seek a highly qualified and motivated Post-doctoral researcher (f/m/d) to drive the development of methods for optoacoustic data
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Artificial Intelligence (AI) in medicine transparent, trustworthy, and clinically relevant? Join the VIOLET Project: a pioneering research initiative to develop an explainable and interoperable methodological
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(PD Dr. Keno Bressem) Your Responsibilities • Development and implementation of generative AI models (in particular diffusion models and variational approaches) for creating explainable patient