227 web-programmer-developer "https:" "https:" "https:" "https:" "https:" "https:" "https:" research jobs at Harvard University
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for candidates with interests in multiscale simulations of complex physical phenomena, from the atomistic/electronic scale to mesocopics and beyond. Of particular interest is the development and application
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Lab in the Department of Immunology at Harvard Medical School, Boston, Massachusetts. The work of the Merkenschlager Lab focuses on germinal center interactions and how they promote the development
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scientists, engineers, and/or doctors! The lab is committed to fostering lifelong learners in an environment that is diverse, inclusive and respectful. Learn more about our lab here: https
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for data quality, record-keeping, and rigor are required. The successful candidate will work with Drs. Jennifer and Alon Oyler-Yaniv to develop an independent research project within the scope of the lab’s
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benefits eligible. 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. With
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data. Prepare and publish scientific manuscripts as a lead author. Present research findings at national and international conferences. Collaborate with other group members and external research partners
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evidence across diverse populations using multi-source and multi-modal data, and accelerating drug discovery by leveraging these rich, integrated datasets. This role offers a unique opportunity to develop
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about the Shih Lab: Learn more about the innovative work led by Dr. William Shih here: https://www.shih.hms.harvard.edu/ . What you’ll do: Develop DNA-based sensors that seed crisscross assembly of single
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of design and innovation on value creation for stakeholders. For more information on D^3/LISH , please visit https://d3.harvard.edu and https://d3.harvard.edu/lish/ . Research Focus: Postdoctoral Fellows
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related quantitative field for a one-year Postdoctoral Research Fellow position. This position involves developing new theory and methods at the intersection of causal inference and network science