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In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits Forschungszentrum Jülich is one of the largest research centers in Europe, with
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and is associated with the management of the Institute's own 500 m² clean room facilities (https://www.igm.uni-stuttgart.de/en/research/clean_room_lab/ ). We are looking for an individual with
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, including gas turbines and combustion engines. We combine advanced computational fluid dynamics, theory, machine learning, high-performance computing, and experimental methods to investigate and model complex
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modeling, and scientific machine learning to enable efficient simulations of complex molecular processes across realistic time and length scales. https://doi.org/10.48550/arXiv.2604.24245 https://doi.org
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. To analyze and control thermoacoustic instabilities, we combine fluid mechanics, acoustics, and combustion with methods from control engineering, nonlinear dynamics, and data-driven modeling (machine learning
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uncertainty, since misclassifications can lead to poor energy management decisions or inappropriate building automation responses. The goal of this project is to design and validate a machine learning framework
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
highly motivated AI Scientist (f/m/x) (best: Computational Pathologist / Machine Learning Scientist for Digital Pathology) to join our efforts in developing AI-driven virtual staining pipelines for cancer
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team explores new applications of AI in collaboration with the diverse scientific disciplines associated with the Helmholtz Association. Within our lab “Applied Machine Learning”, you will play a crucial
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the vibrant Rhine-Main science region, the university can exploit its full potential and showcase its innovative power and dynamics. Its status as a comprehensive university fosters multidisciplinary learning
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” at the Faculty of Humanities. The position is located at an office at the management of the DLA and develops the field of exploring and analyzing analogue archive collections with the help of machine learning