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additionally learn a cutting-edge technique called cyclic immunofluorescence (CyCIF), which allows spatial resolution of different cell states within a tissue in order to understand how tumors are organized and
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Description Join our dynamic research team at Harvard University and spearhead groundbreaking research at the intersection of generative AI, multimodal learning, and Earth sciences. We are seeking a highly
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, plan and lead research projects, acquire and analyze experimental data, supervise and mentor undergraduate students, prepare and submit peer-reviewed journal articles, and present their work at meetings
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. Create a Job Match for Similar Jobs About Harvard University Harvard University is devoted to excellence in teaching, learning, and research, and to developing leaders in many disciplines who make a
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; acquire and analyze relevant data; supervise and mentor undergraduate students; prepare and submit peer-reviewed journal articles; and present their work at professional conferences. They will also be
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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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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
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We are seeking a candidate with expertise in computational biology, machine learning, and/or high-dimensional statistics to work as a postdoctoral research fellow in the Department of Biostatistics
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• Preferred: Familiarity with multiple data science tools and ability to learn new tools as required. • Preferred: Excellent communication and writing skills. This position is funded by an NIH T32 grant
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Health’s Department of Biostatistics is seeking a highly motivated postdoctoral research fellow to pioneer cutting-edge statistical and machine-learning approaches for the analysis and synthesis of diverse