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to staff position within a Research Infrastructure? No Offer Description PhD Position in Physics-Informed Machine Learning for Cardiac Magnetic Resonance The CMR Zurich group at the Institute
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computational sub-team that includes computer scientists and computational PhD students, fostering an interactive environment of technical exchange, code review, mutual support, and collaborative problem-solving
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collaborate with experts in computational mechanics and machine learning. How to apply ?: Please submit a detailed CV, at least 2 recommendation letters or contact information of people who can recommend you, a
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large-scale focused efforts: achieving quantum advantage through coherence-driven materials design and realizing scalable, modular quantum systems. These efforts integrate vertical co-design, from
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large research team. Job Description Primary Duties & Responsibilities: Designs, develops, and implements: Algorithms and computer software for omics-based data sets [high-throughput, massively parallel
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, biomedical data modeling and ontologies, biomedical natural language processing and information retrieval, health artificial intelligence and machine learning, privacy technology, global health informatics
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practices” research themes. The successful candidate will have: a PhD in Translation Studies/Machine Translation; practical experience conducting data-driven research in a machine translation/large language
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University of California, San Francisco | San Francisco, California | United States | about 1 month ago
data scientist, several student learners and a faculty member of the UCSF Department of Epidemiology and Biostatistics. The team has extensive experience with machine learning, artificial intelligence
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discovery, novel algorithmic and statistical techniques for big data management, optimization for machine learning, analysis of information and social networks, fairness, accountability, and transparency in
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL