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Postdoctoral Position in Microbiome, Bioinformatics, and Computational Biology - (250000Y1) The Center for Advanced Microbiome Research and Innovation (CAMRI) within the Institute of Genome Sciences
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Qualifications Minimum Qualifications: Education: A Ph.D. degree in a quantitative field such as statistics, biostatistics, bioinformatics, computational biology, human genetics, computer science, or a closely
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testing and computational simulations (e.g., finite element analysis, fluid-structure interaction). This work will contribute to improving our understanding of valve biomechanics, inform device design, and
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for conducting and supporting research in gastrointestinal carcinogenesis pathways utilizing human biospecimens and preclinical models. The candidate should have experience in molecular and cellular biology lab
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Qualifications Minimum Qualifications: Education: A Ph.D. degree in a quantitative field such as statistics, biostatistics, bioinformatics, computational biology, human genetics, computer science, or a closely
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which early-life gut microbial metabolism shapes host immunity. We welcome applicants with backgrounds in computational biology, molecular microbiology, and/or basic immunology to apply for the following
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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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. degree from an accredited university in biomedical engineering, electrical engineering, neuroscience with interest in imaging sciences or related field. The successful candidate will be self-driven
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. Qualifications a. Qualifications A PhD in Computer Science or Electrical Engineering with biomedical data analysis emphasis or related fields is required. Previous experience in developing novel data mining
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of Maryland School of Medicine, Center for Stem Cell Biology & Regenerative Medicine and Department of Diagnostic Radiology and Nuclear Medicine, for ongoing laboratory research on neural stem cell labeling