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We are seeking a Research Fellow - Data Science professional with strong expertise in machine learning, deep learning and high-frequency physiological signal analysis. This is a unique opportunity to
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active role in providing high quality and innovative teaching in AI, machine learning and related areas. The School has a long-standing track record for world-leading AI, and recent successes place us at
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Stanford University / SLAC National Accelerator Laboratory | Menlo Park, California | United States | about 2 months ago
simulation, reconstruction, and machine learning for ATLAS / LHC data, and other research opportunities in the SLAC ATLAS group, while also demonstrating alignment with the SLAC mission and values ( https
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the application of statistics and machine learning in social science. The position requires no teaching, though teaching opportunities may be provided if requested. When teaching, successful candidates will carry
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will bring deep expertise in clinical trial methodology, protocol development, regulatory strategy, and academic-industry collaboration, with working knowledge of machine learning and artificial
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particular machine learning, to help us apply AI–based applications / solutions to Health Sciences’ problems and drive innovation and digital reinvention. You will join a multidisciplinary team helping to
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-class or that has world-class potential. Essential Research Criteria Qualifications –- a good first degree and a PhD in Psychology, Neuroscience, Machine Learning, Computer Science or a related subject
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Informatics and edge intelligence etc. Must have documented significant Knowledge/Research Background, or Must be able to demonstrate skills on Data Analytics and Machine Learning, in particular on distributed
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, machine learning, programming, software engineering, instrumentation, benchmarking, reproducibility and technological prototyping. The mandatory requirement for technical-scientific proficiency in English
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are built on a foundation of continuous learning and growth . We actively support professional development by providing all full-time staff employees with at least 80 hours of paid time per year and provide