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Field
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Computational Biology, Bioinformatics, or a related field (e.g. statistics, computer science, or quantitative biology). Experience in the application and development of computational methods/tools or machine
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training on specific specialized skills and research methods, and is responsible for the operation of the lab. ● Other duties as assigned. Special Notes: The Research Foundation of SUNY is a private
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areas (i) Non-adiabatic chemical dynamics, (ii) Physics of charge transport in the solid state, (iii) QM/MM methods, (iv) Atomistic classical simulations of macromolecules (v) Biophysics. You can write
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
for Outstanding Mentorship, the MIND (Methods for Incomplete Neurological Data) Lab at UNC-Chapel Hill brings together biostatisticians and neuroscientists to advance methods that accurately estimate disease
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
Hiring Range $62,000 - $65,000, dependent on experience Proposed Start Date 10/20/2025 Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in
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CRSA - Postdoctoral position on high- resolution root zone soil moisture estimation by assimilation of microwave-derived surface soil moisture About UM6P: Located at the heart of the future Green
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summaries (visualizations) using relevant computer software; Assisting with preparing grant applications to secure further funding for relevant projects; Helping establish new projects and research consortia
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(visualizations) using relevant computer software; Assisting with preparing grant applications to secure further funding for relevant projects; Helping establish new projects and research consortia relating
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of natural language processing, machine learning, artificial intelligence, and human-computer interaction. Established within the School of Computer Science, LTI pioneers innovative approaches to understanding
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with various methods that can incorporate domain-based constraints and other types of domain knowledge into machine learning and applying these techniques to problems in computational creativity