40 parallel-processing-bioinformatics "https:" Postdoctoral research jobs at Duke University
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. The Department of Surgery at Duke University is seeking a qualified Postdoctoral Associate. This postdoctoral research position focuses on applying AI, agentic systems, and software engineering to
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. The Department of Biostatistics and Bioinformatics at Duke University is seeking a Postdoctoral Associate to join our research team. In this role, you will collaborate with investigators within and
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research by visiting https://www.theoliverlab.com/ or searching "Oliver TG" on PubMed. Minimum Requirements: Ph.D. in Cancer Biology, Cell Biology, Molecular Biology, Biochemistry, Genetics, Immunology
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acquire new technical expertise as needed. Participate in clinical research efforts involving retrieval, processing, and molecular analysis of patient-derived specimens from prospective clinical trials
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Postdoctoral Associate to be part of an integrated experimental and computational team working to create focused antibiotic prodrugs that treat bacterial infections while sparing the gut microbiome. Building
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infectiousness, treatment and prevention interventions, and realistic behavioral processes to evaluate strategies for reducing HIV transmission. The Postdoctoral Associate will train under the primary mentorship
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processes. Ongoing projects combine molecular biology, neurobiology, biochemistry, and computational approaches to address fundamental questions in RNA regulation. Minimum Requirements: Min Degree
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related field, with strong experience in molecular and cellular biology, molecular biology–related bioinformatics, flow cytometry, and cell culture. The candidate should be highly motivated, organized, and
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and analyzing multidimensional data sets Familiarity with bioinformatic and biostatistical approaches for the analysis of sequencing and proteomic data Desire to conduct team-based science in close
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) Evaluate the implications of these associations for extraction processes; and 3) Assess the critical mineral resource potential of unconventional wastes through geospatial and statistical analysis of supply