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Computational Chemistry, Materials Science, or a related field. Strong background in computational chemistry techniques, including molecular dynamics, quantum mechanical simulations, and machine learning
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Postdoctoral Research Scholar in Machine Learning and Computational Genomics Department of Epidemiology, School of Public Health, University of Pittsburgh The Department of Epidemiology
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: 278964401 Position: 2025 Postdoctoral Research Associate - AI/machine learning for analytical and forensic chemistry Description: The Skinnider Lab at Princeton University aims to recruit a postdoctoral
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FieldComputer science » OtherEducation LevelPhD or equivalent Skills/Qualifications CANDIDATE ’S PROFILE The candidate should possess a PhD in machine learning or computer vision and have a strong publication
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Vision Profiler (UVP), and to analyse its spatial and temporal variability. This will be done by combining different data sources and machine learning (ML). Data used for this ML approach include - a
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obtainable using the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. Furthermore, the postdoc will aid in
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engineering, or related disciplines who are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models
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think outside the box, to learn fast, collaborate effectively, iterate quickly, and work at the interface of both experimental and computational design. Qualifications for Computer Scientists, AI/ML: PhD
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of their research projects, fellows in the Smith lab have the opportunity to work one on one with the PhD biostatistician associated with our laboratory to learn introductory machine learning methodology such as
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qualifications: PhD or equivalent achievement (proof of independent research capability) in Machine Learning, Computer Science, Physics, Mathematics, or a related field Deep theoretical knowledge and extensive