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responses to social experience and pheromone circuit activity in Drosophila. Applicants should hold a PhD in neuroscience or a related field and have experience working with Drosophila. We are particularly
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for this position will be a highly motivated individual with experience in deep learning and medical imaging and a PhD degree in computer science, electrical and computer engineering, biomedical engineering
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of output from global climate models (CMIP-class models) as well as Integrated Assessment Models (IAMs) such as GCAM or PAGE. The candidate must have a PhD degree in a related field, be fluent in computer
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outside Duke University. Preferred qualifications: PhD (completed in the last 1-5 years or PhD candidate) in a quantitative discipline, including Computational Biology, Bioinformatics, Computer Science
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and non-invasive neurostimulation. The strong interdisciplinary and collaborative environment at Duke is ideal for our multi-scale modeling research efforts. An earned PhD and previous experience in
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manuscripts and preparing talks/presentations on the findings. We are interested in outstanding candidates who have a PhD in in organizational behavior or social psychology, with strong experimental research
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. Compliance with all applicable University and departmental policies and procedures. Required Qualifications at this Level Education/Training The Postdoctoral Appointee holds a PhD or equivalent doctorate (e.g
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. Compliance with all applicable University and departmental policies and procedures. Education The Postdoctoral Appointee holds a PhD or equivalent doctorate (e.g. ScD, MD, DVM). Candidates with non-US degrees
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. TERM: 1 year, with the possibility of extension based on performance and funding. QUALIFICATIONS: Applicants should hold a PhD in Political Science, Sociology, Human Geography, Anthropology, or related
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collaborative environment at Duke is ideal for our multi-scale modeling research efforts. An earned PhD and previous experience in computational neurostimulation modeling are required as are excellent