26 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof" "UNIS" Postdoctoral positions at Yale University
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Haven, Connecticut (USA) Introduction: The Department of Biomedical Informatics & Data Science (BIDS) is a new department at the intersection of health sciences and information technology. It develops new
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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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goal is to uncover how mRNA can serve as a building block for tissue engineering and regenerative therapies, with applications in stem cell development, wound healing, and cardiovascular disease
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. Experience working with rodent models is preferred but not required. The successful candidate should have excellent oral and written communication skill, be highly motivated for career development in
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acquired DNA alterations in normal cells and in cancer development. The successful applicant will work independently, perform experiments, develop and optimise assays, and analyse, interpret, and present
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), or reinforcement learning (RL) post-training. Experience with multi-GPU training and a strong working knowledge of reinforcement learning are also required. Familiarity with standard software development tools
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. Opportunities for presentations at local, regional, and national meetings, manuscript writing, grant preparation, and professional development. Qualifications: MD, PhD, PharmD, or equivalent degree. Background in
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. The postdoctoral associate will actively participate in methodological and collaborative research as well as support writing research grants. Professional development will be an integral part of the position
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from internal and external speakers on the latest developments in the field. Location: West Haven VA Medical Center, 950 Campbell Avenue, West Haven, CT 06516 Qualifications: Postdoctoral positions
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-driven projects which aim to understand genome stability and acquired DNA alterations in normal cells and in cancer development. The successful applicant will primarily analyse sequencing data generated by