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Field
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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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collaborate with partners to develop and sustain locally-led, quality health systems, programs and policies. We are dedicated to hiring faculty who are committed to fostering an inclusive, respectful and
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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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stream ecosystems in the Southeastern U.S. over a range of seasonal and strenuous conditions. The candidate will be able to design, develop, and build lab and microcosm-scale experimentation systems
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
the School’s Strategic Plan (https://pharmacy.unc.edu/about/oe/strategic-plan/). Our Vision is to be the global leader in pharmacy and pharmaceutical sciences. Our Mission is to prepare leaders and innovators
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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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at the advanced level. Have training in research procedures. Experience and specialized training necessary to develop and interpret data required for success in research projects. Preferred Qualifications and
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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
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heavy software development component. The successful candidate will perform research in the application of machine learning (ML) techniques to the finite element method (FEM) in the context of composites