68 cloud-computing-"https:" "https:" "https:" "https:" "https:" "https:" "St" "University of St" Postdoctoral research jobs at Duke University
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% to supervising and assisting PhD students. Qualifications • Candidates with a Ph.D. in any area of cognitive neuroscience broadly defined (e.g., Psychology, Neuroscience, Computer Science, or a related field) are
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brain evolution. We employ a multifaceted strategy to bridge developmental neurobiology, RNA biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu
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all voices are heard. All members of our community have a responsibility to uphold these values. Application Materials Required: Further Info: http://www.bme.duke.edu http://www.bme.duke.edu
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skills are particularly welcome. For information about the lab and the PI, please visit: https://www.cellbio.duke.edu/purushothama-rao-tata/ . This will be a minimum one-year appointment, contingent upon
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. Additional Qualifications: • Proficiency in Python, R, SQL, and/or other relevant programming languages; knowledge of Java or C++; familiarity with shell scripting, cluster or cloud computing infrastructure
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found at http://jewishstudies.duke.edu . Further information about post-doctoral services at Duke may be found at: http://postdoc.duke.edu/ . Previous applicants are eligible to re-apply. Candidates from
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of all individuals are respected, and that all voices are heard. All members of our community have a responsibility to uphold these values. Application Materials Required: Further Info: https
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discovery and computational tools. The successful applicant will lead a research project and will have the opportunity to mentor students. Candidates must hold a PhD or anticipate completion of a PhD prior to
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career. The appointment is not part of a clinical training program, unless research training under the supervision of a senior mentor is the primary purpose of the appointment. The Postdoctoral Appointee
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simulations and multiscale spatial-omics data. • Integrate uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments