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Field
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remodeling. We employ advanced computational and theoretical techniques, such as large scale flow network simulations, machine learning, and methods from topological data analysis, to a broad set of problems
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-sensor hyperspectral data for crop disease detection and monitoring. Develop machine learning or physics-informed models to retrieve reliable spectral and/or biophysical features, from multi-scale
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can contribute through their research, teaching and service to the intellectual diversity and excellence of the Liberal Studies community. Liberal Studies Postdoctoral Faculty Fellows teach two courses
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factors. Though not required, we are particularly interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and
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mathematicians and computer scientists at the University of Southampton, the University of Oxford (lead node), Imperial College London, Queen Mary University of London, Durham University, and the University
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transferring learning from other geographic regions and data types, machine learning methods, Bayesian inference and interrogation theory. The post may involve travel to Iceland and Italy in support of your work
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and development activities in the fields of atomistic simulations, including density functional theory, machine learning, and molecular dynamics. The work involves theoretical and experimental research
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-Coding Genome Edits: Develop innovative machine learning approaches for designing precise non-coding genome edits, focusing on how non-coding alterations influence gene regulation and cellular function
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team. Preferential factors: academic performance, with a focus on Machine Learning and Biomedical sciences previous experience (e.g., research, professional, lecturing) in the domains of the grant
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to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig