19 machine-learning-modeling PhD positions at Technical University of Munich in Germany
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) to work on the development of an Amazonian Early Warning System (AmEWS) integrating Earth Observation data, process-based ecosystem models, and advanced machine learning approaches. The position is embedded
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07.04.2026, Academic staff PhD position at the interface of computational physics, machine learning, and experimental reactor design. The project focuses on developing PINN-based simulations
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to defining specifications and requirements for the FDI system • Development and evaluation of model-based and machine learning-based FDI algorithms, in close exchange with relevant stakeholders • Close
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Optimization (DPO) and reinforcement learning from human feedback, building preference datasets together with clinicians - Build and run a Red Team process with physicians, computer scientists, and patient
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processing parameters. You will develop machine learning models to analyse experimental datasets and uncover structure-function relationships that determine membrane performance. By combining statistical
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, quality, scaling, and evaluation. The Professorship for Machine Learning at TUM works on machine learning, artificial intelligence, and information processing, with a current focus on foundation models
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Learning at TUM works on machine learning, artificial intelligence, and information processing, with a current focus on foundation models, data-centric research, and applications to scientific and medical
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Responsibilities • Develop Probabilistic Machine Learning Models to integrate graphs and food-related omics data • Multi-omics integration using graph-structured prior knowledge • Analyze food-related (multi-)omics
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qualified women. About the position The position contains both teaching duties and participation in research projects. The research project topics focus on improving object recognition through computer vision
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biological models. This position involves the optimization, and operation of an innovative multimodal microscope, as well as close collaboration with experts in the biological sciences for its application