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27.10.2025 Application deadline: 30.11.2025 Are you excited about the possibility to explore ethical, philosophical, legal, epistemic or social implications of using machine learning in different
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-party research funding are expected. We are particularly interested in a candidate in any field of economics who leverages state-of-the-art machine learning and causal inference methods to innovative
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Max Planck Institute for Physics, Garching | Garching an der Alz, Bayern | Germany | about 17 hours ago
to cover a wider variety of physics use cases. Developing methods to make machine-learning-based models portable and interoperable. Leading the definition of containerized and networked “Models as a Service
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electron imaging integration of motion analysis in collaboration processing of images in preparation for image-based modelling Your profile PhD in physics, materials science, computer science, applied
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and others) Analysis of the experimental data, ideally connecting to our machine learning tools Presentation of scientific results on conferences and in publications Requirements PhD degree in physics
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and supervise PhD candidates and student assistants Contribute to project governance and represent the project in steering committee meetings Your profile: An outstanding doctoral degree (e.g., magna
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-scale research facilities (e.g. DESY, ESRF), including coordination and setup of experiments Development of data workflows and analysis strategies (in collaboration with our machine learning team
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of German is not required, it will be considered a plus. Specific Requirements PhD degree in physics or chemistry, or equivalent Interest in materials science Good written and spoken English Ability to work
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-XRF, Raman, FTIR in reflection mode) to enable multimodal data fusion and automated material characterization. • Apply and further develop machine-learning and statistical models (e.g. PCA, SAM
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institution of TUM Campus Heilbronn that uses data to answer relevant questions and solve real-world problems. It brings together fundamental, methodologically driven research in optimization, machine learning