127 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" Fellowship positions at Harvard University
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of years post PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW for purposes of collective
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salary, which is determined by the number of years post PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic
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Department of Molecular and Cellular Biology, Harvard University https://projects.iq.harvard.edu/bellonolab Explore molecular mechanisms underlying unique adaptations and behaviors. Fellows have freedom to ask
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, and AI/machine learning would be helpful for the role. Experience with participant recruitment and retention as well as clinical human subject studies is a plus. Special Instructions Application
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computational approaches. See our lab web page (https://projects.iq.harvard.edu/gaudetlab ) for more information about our publications and research interests. Basic Qualifications Candidate must hold a PhD in
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such as: Causal inference and the design and analysis of experiments Reinforcement learning and sequential decision-making Analysis of complex systems, networks, and large-scale data Machine learning
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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date to be determined. Basic Qualifications A PhD related to programming languages by the start date. Experience in machine learning and formal verification. Individuals with a demonstrated track record
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. Cross-Disciplinary Fellowships (CDF) are for applicants with a Ph.D. from outside the life sciences (e.g. in physics, chemistry, mathematics, engineering or computer sciences), who have not worked in
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date to be determined. Basic Qualifications A PhD related to programming languages by the start date. Experience in machine learning and formal verification. Individuals with a demonstrated track record