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Field
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31.07.2026, Academic staff We are seeking a researcher in Scientific Machine Learning (SciML) to join the project "Data science at scale" at the Technical University of Munich, Germany. Ideal
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and lifetime validation. This position is part of a twin-PhD collaboration between Forschungszentrum Jülich (FZJ) and the French Alternative Energies and Atomic Energy Commission (CEA). The two projects
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isotope probing of microbial communities Sequence analysis of microbial communities Planning and participation in fieldwork (approx. 3 months) on the Antarctic Peninsula, Carlini Station Collaboration with
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evaluation strategies. In close collaboration with chemists, engineers and data scientists, a platform is being developed that combines materials development, process optimisation and machine learning
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magnetic resonance methodology and instrumentation. Within the CRC, ample opportunities exist for learning, networking, and collaboration. Salary will be in accordance with guidelines of the Deutsche
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month 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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data • Design clinically meaningful benchmarks and robust evaluations • Publish at leading machine learning and medical AI venues • Collaborate with clinicians, computer scientists, and European partners
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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