102 parallel-computing-numerical-methods-"Multiple" Postdoctoral positions at Rutgers University in United States
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addressing evolutionary medicine and human behavior. This is a calendar year position. Candidates with experience in quantitative, laboratory, or field-based methods and/or theoretical expertise in
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, PETS has developed into a major research group, recruiting numerous new core, part-time, and adjunct faculty, and an experienced team of highly trained administrative and analytic staff. The current PETS
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motivated Postdoctoral Associate to contribute to multiple projects focused on the contributions of genetic and environment influences to the development of substance use and disorders and related behavioral
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coagulation factors in multiple physiologic and disease contexts. Participates in the evaluation of existing procedures, suggesting revisions where necessary and proposing new procedures where required. Assists
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skills. Multiple resources for career development to aid with these requirements will be provided by the PI and the Office of Postdoctoral Affairs. Among the key duties of this position are the following
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addressing evolutionary medicine and human behavior. This is a calendar year position. Candidates with experience in quantitative, laboratory, or field-based methods and/or theoretical expertise in
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. The candidate must stand during most of the duration of performing behavioral experiments. The candidate must interact effectively with a personal computer for multiple hours per day. Overview Statement Posting
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interaction methods like the split ubiquitin yeast-two hybrid method and SIM microscopy training will be needed. The individual will report directly to the laboratory Principle Investigator (PI). The position
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inference methods in these data, assemble and summarize relevant literature, and will be encouraged to develop their own independent research program in maternal and child health and/or housing and
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. The successful applicant will work in the areas of causal inference and statistical learning with high-dimensional observational data, including development of statistical and computational methods, and