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Field
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objectives of the project are to (i) deploy network analysis methods to genomic data (50%), and (ii) develop such algorithms including community detection algorithms (50%). PhD in computer science, engineering
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. We provide mechanistic, individualized, preventive and multidisciplinary knowledge on a topic that is a global threat to societies: shortened health span and an aging population. Delaying age-related
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individually, make a real difference. The role Applications are invited for a Research Fellow position to support the design of a predictive health management (PHM) module for a novel steer-by-wire system aimed
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Statistics; alternatively, a master’s degree in Biostatistics or Statistics with broad experience working as an applied statistician in health research. Proven comprehensive understanding of biostatistical
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who will join us in our mission to improve health equity and well-being for all throughout the state of Indiana. Basic Qualifications Ph.D. or terminal degree in biomedical engineering or related field
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position for a Research Associate/Fellow (Data Scientist) in Data Science at the Department of Family Medicine (DFM) within the National University Health System (NUHS). The role is focused on leading data
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poroelastic cartilage material properties and the lubricating film formed between contacting surfaces facilitates this phenomenon. This is important in understanding joint health and the adverse effects
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research as well as behavioral health informatics. The fellow will receive mentorship from faculty investigators toward the development of an independent program of research. Training will be provided in
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-renewable-energy-engineering Skills & Experience: A PhD in Computer Science or a related field. Thorough theoretical background in machine learning and deep learning. Demonstrated experience in developing
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Computer Science Position Description The Harvard Center for Research on Computation and Society (CRCS) solicits applications for postdoctoral fellows for the 2024-2025 academic year. CRCS provides