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Field
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of Michigan is offering a postdoctoral research position to investigate cell and immunobiology of tissue fibrosis. This opportunity resides within a multi-disciplinary research program that couples clinical
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, Biostatistics, Computer Science, Statistical Genetics, or a related quantitative field (by the time of appointment). Strong background in statistical or machine learning methodology, optimization, or high
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research environment. Training will be provided where needed. Our research is supported by state-of-the-art core facilities for cryo-EM, proteomics, genomics, and flow cytometry, as well as high-performance
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students to drive forward a diverse research program focused on machine learning and policy evaluation. The following tasks are designed to culminate in co-authorship on research papers: Plan and perform
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 18 days ago
of performance in the program will be provided by Dr. Marraccini and KickStart Venture Services, with a focus on considering commercialization at the onset of research. Grant writing: The fellow will be encouraged
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. The Successful Candidate Will Exhibit research creativity, breadth and depth of knowledge, and the ability to produce high-quality work. Excellent written and oral communication skills. Ability to work in a team
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, Social Sciences , Biomedical Informatics , Causal Inference , Computational Social Science , Data Science and Information , Data Visualization , Deep Learning , High dimensional Data , Large Language
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The National Energy Research Scientific Computing Center (NERSC ) at Berkeley Lab seeks a highly motivated Postdoctoral Researcher — Scientific Machine Learning (NESAP) to join the Workflow
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effect prediction. The fellow will work under the mentorship of Dr. Alex Luedtke and collaborate with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy
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effect prediction. The fellow will work under the mentorship of Dr. Alex Luedtke and collaborate with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy