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regulation of striated muscle, genetic cardiomyopathies, and the pathophysiological mechanisms of atrial fibrillation. This position will provide the opportunity for the postdoctoral fellow to develop and
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fellow to develop and pursue independent projects during their training. The Barefield lab uses mouse models and human induced pluripotent stem cell derived cardiomyocyte models of genetic cardiovascular
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, Texas, invites applications for a postdoctoral researcher position as part of the NSF-funded Research Training Group, “RTG: Numerical Mathematics and Scientific Computing.” (https://rtg-nasc.rice.edu
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
the nation for federal research expenditures as well as for federally funded social and behavioral sciences research and development. Here at Carolina, our highly skilled postdocs play a vital role in our
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postdoctoral training and career development; serve as the central resource office for postdoctoral questions and issues for faculty, supervisors, mentors, and prospective and current postdocs; build a community
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position in MR Physics. This successful applicant will help with development of MR imaging methods to study tumor metabolism and response to therapy. The research in the MRRF is focused on the development
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photosynthesis to join the new pilot study of Generative Pretrained Transformer for genomic photosynthesis (GPTgp). The GPTgp project aims to develop a foundational holistic model of photosynthesis that will scale
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cardiomyopathies, and the pathophysiological mechanisms of atrial fibrillation. This Position will provide the opportunity for the Post Doctoral Research Scholar to develop and pursue independent projects during
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for 2D Quantum Magnets) collaboration. While located at the facility, the role is not primarily experimental; instead, the successful candidate will develop forward models, synthetic datasets, and AI tools
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, atmospheric signals), data fusion across sensing modalities, and development of scalable machine learning pipelines. Work will be entirely computational and based in Seattle, with no field deployment