12 programming-"SciLifeLab" Postdoctoral positions at Harvard University in United States
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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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. Successful candidates will be expected to contribute to technique development/material synthesis, plan and lead research projects, acquire and analyze experimental data, supervise and mentor undergraduate
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programs, including MD and/or PhD programs, as well as industry positions. Responsibilities will be: Mouse husbandry, genotyping, dissection Molecular biology, e.g. Western blotting, cloning Cyclic
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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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Experience with signal processing, statistical data analysis, or machine learning techniques. Proficiency in scientific programming (e.g., Python, MATLAB, or similar). Ability to work independently while
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strong research program in astrophysics and provides a stimulating environment. Women and minorities are strongly encouraged to apply. AAE/EOE. Special Instructions Letters of reference should be submitted
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. Successful candidates will be expected to contribute to technique development/material synthesis, plan and lead research projects, acquire and analyze experimental data, supervise and mentor undergraduate
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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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conferences and/or leading scientific journals. Excellent programming skills and hands-on experience with leading machine learning frameworks (e.g., TensorFlow, PyTorch). Practical experience with cloud