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Details Title Postdoctoral Research Position in Computational Biology School Harvard T.H. Chan School of Public Health Department/Area MIPS/Environmental Health Position Description The Haber Lab in
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The Postdoctoral Diversity Enrichment Program (PDEP) provides $60,000 over three years to support the career development activities and success of underrepresented postdoctoral fellows in a degree
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, engineering, computer science, and machine learning. The postdoctoral fellow will assume scientific leadership for one observatory site and will: Lead site commissioning, including performance verification and
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for extraterrestrial techno signatures and analysis of data from the Galileo Project observatories. The successful candidate will have access to local computing facilities and will have the opportunity to interact and
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voluntary benefits. Most coverage begins as of your start date. Work/Life and Wellness: Child and elder/adult care resources including on campus childcare centers, Employee Assistance Program, and wellness
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of biomechanics and computer vision to document the wingbeat frequencies and phototactic behaviors of diverse insects under diverse contexts. Candidates will be expected to plan and lead behavioral experiments
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(FIC ) National Center for Complementary and Integrative Health (NCCIH ) Division of Program Coordination, Planning and Strategic Initiatives, Office of Research Infrastructure Programs (ORIP ) National
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) Division of Program Coordination, Planning and Strategic Initiatives, Office of Research Infrastructure Programs (ORIP ) Special Note: Not all NIH Institutes and Centers participate in Parent Announcements
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, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong research skills, evidenced by high-quality publications in top-tier machine learning/AI
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data in near-term era quantum computers. Applicants with backgrounds in quantum information or particle physics are both encouraged to apply. Candidates with strong expertise in machine learning, quantum