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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 2 months ago
environment. Desirable qualifications Strong knowledge of statistics, statistical learning, or probabilistic modeling. Experience collaborating with experimental scientists, biologists, or clinicians. Interest
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affiliated with the graduate school. This collective expertise ensures comprehensive guidance and mentorship throughout the academic journey. Collaboratively, the student and their TAC engage in formulating a
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of measurement and control at the quantum level. Active engagement in shaping the scientific agenda of the cluster and close collaboration with regional and international partners - particularly the Physikalisch
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close collaboration with a deep-tech startup. You will work in an internationally recognized research environment with access to state-of-the-art cleanroom facilities and collaborate with leading academic
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learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents
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of machine learning and clinical oncology, with access to a large multimodal research dataset, substantial GPU resources, and a collaborative scientific environment. Your tasks Design and implement LLM-based
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Albert-Ludwigs Universität Freiburg | Freiburg im Breisgau, Baden W rttemberg | Germany | about 1 month ago
element method, or scientific programming is an advantage but not a strict requirement. A collaborative mindset, curiosity, and willingness to travel for research visits and network events. Excellent
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collaborations within and beyond the department. In terms of teaching, the subject of experimental physics is to be represented – eventually across its full breadth – in the bachelor's, master’s and teacher
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Max-Planck-Institut für Struktur und Dynamik der Materie | Hamburg, Hamburg | Germany | 3 months ago
more than 30 countries, MPSD combines experimental and theoretical research across five scientific departments in a highly collaborative and interdisciplinary environment. To strengthen our IT and Scientific
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Campus (LLEC). Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning Data analysis considering uncertainties, missing data