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multiple affiliations. Be You. Duke School of Medicine’s interdisciplinary Duke Molecular Physiology Institute (DMPI), is seeking a highly motivated Postdoctoral Fellow with a PhD in computational biology
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. The investigator collaborates with interdisciplinary clinical teams to integrate these computational tools directly into hospital workflows. Routine duties include algorithm optimization and the evaluation of model
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the context of critical illness. This position focuses on computational modeling of host-response mechanisms using high-dimensional multi-omics datasets. The fellow develops novel computational pipelines
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addition to pursuing their own research agenda, we seek applicants with experience in survey design and computational methods, and working with complex large-scale data. Successful candidates will have completed a Ph.D
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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
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. Occupational Summary The David Lab at Duke University (www.ladlab.org ) is recruiting a postdoctoral fellow to join an established research group developing and applying DNA sequencing and computational
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in vitro and in vivo studies, including experiments involving ionizing radiation. · Utilize genetically engineered mouse models (GEMMs) and other mouse models of brain tumors. · Conduct experiments
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—induced by targeted drugs—can serve as a powerful therapeutic avenue in prostate cancer. Working under the mentorship of a Duke faculty leader, you will develop your independent research program, contribute
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., D.V.M.) This position is Onsite. The work is performed on-site or at a designated assignment location. Be Bold. Help to operationalize NeoPBl/NeoSelect technology for the Klemen Lab. Assist in development
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regional leadership in biostatistics, genomics, biomedical informatics, artificial intelligence and health data science. The Postdoctoral Associate will conduct research in statistical machine learning and