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, United States of America [map ] Subject Areas: Environmental Studies / Natural Resources Science and Management Climate Change Starting Date: 2026/08/01 Appl Deadline: 2026/04/30 11:59PM * (posted 2025/10/15, listed until
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Duke University, Economics Position ID: Duke-Econ-PD2025 [#30253] Position Title: Position Type: Postdoctoral Position Location: Durham, North Carolina 27708, United States of America [map
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biology. This NIH-funded position is available to study the role of nuclear pore proteins in the regulation of chromatin architecture during neuronal differentiation. The successful candidate will have a
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genomics, metabolomics, or microbiome analysis Computer science, particularly machine learning, artificial intelligence, data science, or computational biology Mathematics or statistics, with experience in
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, United States of America [map ] Subject Areas: Environmental Economics Natural Sciences Forestry Appl Deadline: none (posted 2025/08/28) Position Description: Apply Position Description Dr. Jeffrey R. Vincent, the Clarence
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The research will be studying skeletal muscle biology and function in regulating other tissues, using cellular, molecular, and model animal (transgenic mice) approaches. Specifically, the postdoctoral research
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will be studying skeletal muscle biology and function in regulating other tissues, using cellular, molecular, and model animal (transgenic mice) approaches. Specifically, the postdoctoral research
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mentoring, if needed. Minimum Qualifications The candidate should have a Ph.D. in Engineering, Applied Mathematics, Computer Science, or a related area. Experience 0+ years of postgraduate experience. Skills
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. · The appointment is not part of a clinical research training program, unless research training under the supervision of a senior mentor is a primary purpose of the appointment. · The appointee works under
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healthcare. Qualifications Required: PhD (or equivalent) in computer science, statistics, biostatistics, electrical/biomedical engineering, or related quantitative field. Strong background in machine learning