126 machine-learning "https:" "https:" "https:" "https:" "https:" Fellowship research jobs at Harvard University in United States
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in machine learning and formal verification. Individuals with a demonstrated track record in scientific research, which can be evidenced through publications, technical reports, or impactful software
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, Massachusetts campus. The lab's website is: http://projects.iq.harvard.edu/evolutionary_genetics/home Basic Qualifications: A doctoral degree is required for this position. Desired qualifications include research
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on research activities at our research labs. D^3 conducts research at the intersection of academia and practice. For more information on D^3, please visit https://d3.harvard.edu/labs . D^3 is looking
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contribute to overall lab operations. The applicant will be a collaborative, impact-focused problem solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber
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Additional Qualifications Strong confidence with and/or facility to learn Matlab or Python-level programming Strong interest and experience in systems neuroscience, electrophysiology, or primate behavior
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will be given the added opportunity (funded and encouraged) to collaborate with and visit groups in sister institutions (https://www.origins-federation.com/). Applications consisting of a CV and a 4-page
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DNA elements and transcriptional and chromatin remodeling machinery in gene regulation. More information about the lab and specific research areas can be found at https://adelman.hms.harvard.edu
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devoted to excellence in teaching, learning, and research, and to developing leaders in many disciplines who make a difference globally. The University, which is based in Cambridge and Boston, Massachusetts
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of References Allowed Keywords statistics, biostatistics, computer science, economics, health care policy, causal inference, machine learning
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/guidelines . Minimum Number of References Required Maximum Number of References Allowed Keywords statistics, biostatistics, computer science, economics, health care policy, causal inference, machine learning