27 algorithm-development-"St"-"St" Postdoctoral research jobs at Technical University of Munich
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07.09.2025, Wissenschaftliches Personal We are seeking a PhD or Postdoctoral researcher to join the AI in Orthopaedics group (TUM & TUM University Hospital). The position focuses on developing
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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algorithmic algebra. For more information about the TUM Department of Mathematics, please visit our website: https://www.math.cit.tum.de/en/math/home/. The position is a full-time position (100%), initially
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research studies for automated image analysis. In particular, you will: Plan, develop, and implement AI/ML algorithms for pathology image analysis. Integrate multi-modal data (e.g., genomics, clinical data
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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization
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MesaPD to solve complex multiphysics problems. The coupling is done across package boundaries. This also requires more sophisticated approaches in load-balancing. Finally, the newly developed algorithms
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this interdisciplinary project, we are looking for a strong candidate to contribute to the development of quantum algorithms and applications, focusing on quantum walks and quantum machine learning on graph structures
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“NANeurO,” which focuses on the development of injectable nanoelectrodes for wireless and minimally invasive neural stimulation. About us The NEN Lab works with new materials for wireless communication with
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vibrant and interdisciplinary research environment at TUM Access to unique datasets and collaboration with a well-established European network Opportunities for professional development and academic career
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of green hydrogen by use case. Beyond the economic feasibility, a particular focus of this position will lie on the evaluation of different development pathways when deploying green hydrogen infrastructure