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Field
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, i.e., machine learning models explicitly constrained by physical laws (e.g., conservation of mass, momentum, or energy) or designed to integrate physics-based models and data-driven learning
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Fellow in machine learning available at Department for Informatics with the research group Digital Signal Processing and Image Analysis as part of Visual Intelligence , Norway’s leading research centre
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Data Science, Human Centered AI, and the SLAC Machine Learning Program. KIPAC also has strong ties and active collaborations with theorists at LITP @ Stanford and SLAC. The positions offer a competitive
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collaborative mindset and excellent communication skills in English. Significant Advantage: Previous experience with adversarial machine learning, offensive security, or publications in top-tier conferences (e.g
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and Machine Learning tools and algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability
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part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300
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Tool (SWAT model), and/or Noah Land Surface Model with Multi-Parameterization Options (Noah-MP model). Experience in either developing or applying Artificial Intelligence and Machine Learning tools and
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computational, theoretical and/or observational projects, to develop and deploy cutting-edge machine-learning and AI methods for astrophysics and cosmology, enabling precision tests of fundamental physics with
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of the Saez Rodriguez group is to acquire a functional understanding of the deregulation of signalling networks in disease and to apply this knowledge to develop novel therapeutics. We focus on cancer, auto
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(or equivalent) in Computer Science, Machine Learning, Mathematics, or a related technical field. For Postdoctoral Fellows: A completed PhD in one of the fields mentioned above and a strong publication record