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Field
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machine learning for time series, geospatial data or dynamic models; ideally experience with deep learning frameworks (e.g., PyTorch). Strong analytical and conceptual skills for designing and interpreting
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Your Job: In this position, you will be an active member of our Simulation and Data Lab for AI and Machine Learning in Remote Sensing, which aims to strengthen interdisciplinary research and
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Karlsruher Institut für Technologie (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 2 months ago
organisation. Where to apply E-mail jobportal@careerservice.kit.edu Website https://jobs.pse.kit.edu/en/jobs/169369/scientific-associate-fmd-in-computer-sc… Requirements Additional Information Website
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and infrastructure facilities of the Leibniz Association . The DPZ’s Primate Data Science (PRIDAS) platform seeks a position for Data Scientists – Computer Vision (m/f/d). The position is suitable
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equivalent university degree in psychology or a related discipline (computer science, neurosciences, biology, cognitive sciences) • Skills in perception psychology, neurosciences, machine learning, tracking
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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. MATLAB, C/C++, Python. Highly motivated and keen on working in an international and interdisciplinary team. Applicants with strong background in the following fields are preferred: Machine Learning Formal
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environments Practical experience with machine learning and AI methods and an interest to learn, adapt and apply ML methods to challenging problems in mass spectrometry. Independent and cooperative working in
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developing a digital twin, employing machine learning and numerical computations of atomistic processes. At IKZ, a kinetic Monte Carlo tool has been developed in the programming language julia. This allows a
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, you will be an active member of our Simulation and Data Lab for AI and Machine Learning in Remote Sensing, which aims to strengthen interdisciplinary research and operational services by bridging