231 machine-learning "https:" "https:" "https:" "https:" "https:" positions in Germany
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Field
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- When and where do we reach the limits of adaptation to riverine flood risk?”. You have experience in machine learning, programming and flood risk research. If so, we encourage you to apply! You will
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Your Job: At the Institute for Advanced Simulation – Data Analytics and Machine Learning (IAS-8) we are looking for a PhD student in machine learning to work within a project linked to the
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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mechanisms of learning, memory formation, perception, and behavior. Researchers with a proven track record in neuroengineering and related fields — including neuro-inspired hardware, brain-machine interfaces
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Simulation – Data Analytics and Machine Learning (IAS-8) at Forschungszentrum Jülich, which is dedicated to pushing the boundaries of data science theory and application. Our research spans from use-inspired
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programming task within the thematic context of the advertised position: https://www.hpc.uni-wuppertal.de/de/peter-zaspel/challenge-in-bi-molecular-machine-learning/ Employment conditions This is a
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Job description: DESY The CMS Quantum Computing group develops generative machine learning models for detector simulations, specifically the simulation of showers in calorimeters: Proof-of-principle
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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assessment, programming and machine learning. If so, we encourage you to apply! You will develop exposure and physical vulnerability maps for past and future (1970-2100) and integrate these into a flood risk
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning