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Baltimore, Maryland Position Description: The Division of Endocrinology, Diabetes, and Nutrition (EDN) at the University of Maryland School of Medicine, Baltimore (UMB), is recruiting a computational
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, School of Medicine is an interdisciplinary, multi-departmental team of collaborative investigators with a broad research program related to the basic and translational sciences, genomics, epigenetics, and
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, strongly motivated researcher with knowledge of MR physics. Working knowledge of computer languages (e.g. Matlab) and statistical analysis software for imaging processing of structural and functional MRI
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. IGS at the University of Maryland, School of Medicine is an interdisciplinary, multi-departmental team of collaborative investigators with a broad research program related to the basic and translational
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are currently recruiting motivated Postdoctoral Fellows with a Ph.D., M.D. (or equivalent) and a background in Computer Science, Electrical Engineering, Biomedical Engineering, or related fields. Candidates who
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; Embraces and actively promotes an inclusive and equitable work environment. Experience conducting mixed-methods research, including quantitative and qualitative analytical skills; Proficient computer skills
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behavior, neurophysiological recordings, molecular biology, and/or computational modeling is preferred, but not required. UMB employees are strongly encouraged to follow all CDC recommendations related
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, M.D., Associate Professor of Pediatrics and of Medicine; Co-Director of the Immunoepidemiology and Pathogenesis Unit within the Malaria Research Program: mtravass@som.umaryland.edu . Further information
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integration of transplanted stem cells. We expect the fellow to be highly motivated, independent and innovative. The applicant can expect to receive career mentoring from faculty of the Program of Image Guided
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scholarly activity in areas of interest. Scholarly activity is defined broadly and includes, for example, proteomics, LC-MS/MS-based approaches, data analysis and informatics for mass spectrometry-based data