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findings at conferences and professional meetings Collaborate with leading researchers within and outside UVA Qualifications U.S. citizenship required Ph.D. in Data Science, Statistics, Computer
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Business, Commerce, Innovation, Labor Computer Science, Data Science, Information Science Environmental Sciences and Geography We are particularly interested in transdisciplinary research that addresses key
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experimental and computational angles. The candidate will have opportunities to drive his/her own research project(s), develop fellowship applications, write manuscripts, mentor junior trainees, and learn skills
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and membrane protein complexes • Familiarity with Linux, MATLAB, Python, or other computational tools is a plus This position provides an excellent opportunity to work on high-resolution structural
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Charlottesville area, visit UVA Life and Embark CVA . This is primarily a sedentary job involving extensive use of desktop computers. The job does occasionally require traveling some distance to attend meetings
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addition to the above job responsibilities, other duties may be assigned. Minimum Qualifications – Education: Graduate of an accredited nursing program required. Bachelor of Science inNursing required within 5 years
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assistants. Physical Demands This is primarily a sedentary job involving extensive use of desktop computers. The job does occasionally require traveling some distance to attend meetings, and programs. Salary
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electrical engineering, biomedical engineering, computer science, medical physics, neuroscience, or a related field. While this is the preferred background, highly qualified candidates from other scientific
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of national research infrastructures. The ideal candidates will have a PhD in a discipline closely related to computational social science by date of appointment (e.g. network science, computer science, data
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research program sponsored by federal agencies such as the U.S. Department of Defense and the National Science Foundation (NSF). The focus of the research is on developing innovative physics-informed deep