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associate to develop deep learning algorithms: 1) to model 3D protein complex structures and impact of mutations/PTMs on protein structure and interactions; and 2) to dissect functional elements (e.g
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the appointment start date; demonstrate strong expertise in computational biology or data-driven modeling, with experience in one or more of the following areas: machine learning or deep learning, structural
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skills such as structural equation modeling, multilevel modeling, longitudinal data analysis, and/or categorical analysis (e.g., growth mixture modeling) using R, Mplus, and/or SAS. Preferred
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Research. The REDES team works on projects that examine and transform the interconnections, structures, and transition points that are critical to creating effective learning and work systems within
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to supporting the Postdoctoral Associate’s career advancement. The PI will provide structured mentorship, regular feedback, support career growth, including networking opportunities, conference participation, and
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modeling, climate data analysis, and natural resource decision-making. The successful candidate will use data from eBird and structured monitoring programs to identify priority areas for migratory forest
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experience with species distribution modeling, climate data analysis, and natural resource decision-making. The successful candidate will use data from eBird and structured monitoring programs to identify
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physics, electronic structure theory, hydrodynamics, machine learning, materials science, statistical physics, strongly correlated electrons, quantum information. While this fellowship targets theorists
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health and well-being, cognitive aging and dementia, loneliness and social isolation, and integrative data analysis. Strong analytic skills such as structural equation modeling, multilevel modeling