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found on our web page, fds.yale.edu. Yale’s Data Science Initiative has supported the rapid growth of the departments of Statistics & Data Science and Computer Science, as well as many interdisciplinary
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(SUDs), psychiatric conditions, and other behavioral and lifestyle characteristics that impact human health using large datasets and biobanks including the Million Veteran Program (MVP), the SUD working
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, quantum information science and related fields (non-equilibrium dynamics, quantum simulation with tensor networks, etc.). A Ph.D. in Physics or a closely related field is required. The initial appointment
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where it would be cost-effective to screen and (iii) incorporating multi-omics data to better identify at-risk individuals beyond lifestyle and environmental approaches alone. Our research program has
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, computer science, data science, social sciences, public policy, or a relevant interdisciplinary area. Interest in addressing the governance, ethical, legal, and societal implications (GELSI) posed by digital
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modeling. However, interested candidates with a strong computational background and interest in getting involved in medical imaging and preclinical models are also strongly encouraged to apply. A PhD in
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School of Medicine. We develop and apply innovative experimental and computational approaches to study cellular heterogeneity and its impact on tissue function in health and disease. Our interdisciplinary
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Post-Doctoral Position in Deep Learning for MRI Reconstruction at Yale University Title: Postdoctoral Associate, Yale School of Medicine Department/Division: Radiology and Biomedical Imaging, Bioimaging Sciences Position Description: Join an exciting effort to develop a low-field, low-cost, MRI...
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inflammation, multiparameter flow cytometry, and bioinformatics/computational biology is desired. Please send curriculum vitae, three names of reference and a one-page summary of research background and
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of mutational processes in human health and disease. We are an interdisciplinary team of experimental, computational, and clinician scientists allowing us to generate and analyse complex data from emerging