282 data-"https:" "https:" "https:" "https:" "https:" "UCL" "UCL" research jobs at Harvard University in United States
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. Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . Minimum Number of References
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at the intersection of academia and practice. For more information on D^3, please visit https://d3.harvard.edu/labs . D^3 is looking for candidates with diverse backgrounds and/or new perspectives. There are no
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Genomics at Harvard Medical School Several positions are available in the Park Lab (https://compbio.hms.harvard.edu/ ). The aim of the laboratory is to develop and apply innovative computational methods
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include conducting survival surgeries in rodents, performing electrophysiological recordings, developing and prototyping sensing hardware, and executing structured data collection protocols. The candidate
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solver who wants to be part of a dynamic team. Information about the Church Lab: Learn more about the innovative work led by Dr. George Church here: https://churchlab.hms.harvard.edu/ , https
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labs working on research at the intersection of academia and practice. For more information on D^3, please visit https://d3.harvard.edu . Business, the global economy, and societies around the
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membranes. Please see https://blacklow.hms.harvard.edu/ for additional information on areas of research. We welcome applications from recent PhD graduates who are interested in these or related fields
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of cilia. More information about the lab and specific project areas can be found at https://brown.hms.harvard.edu/research. We welcome applications from recent PhD graduates, particularly those who may
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,” and we love challenges. For more information, discover our technologies , catch up on our recent news , or watch our latest videos . About this Role: The lab of Don Ingber, M.D., Ph.D., Founding
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees