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Field
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to deal with many-body problems. In that context, the number of degrees of freedom required to fully describe a quantum system grows exponentially with the number of particles, which quickly renders
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management, analysis, writing and dissemination. The selected candidate will be expected to contribute substantially to academic publications, both as first author and as co-author, and to participate in
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international team of experts Assisting with project coordination and management Developing multiple peer-reviewed manuscripts as a lead-author and a co-author Requirements: PhD in ecology, disease ecology
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are contingent upon formal conferral of the Ph.D. Preferred Education and Experience Preferred Education and Experience Ph.D. in Neuroscience, Cognitive Neuroscience, Computer Science, Data Science, Engineering
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quantitative and data analysis skills, the ability to work creatively and independently, and a strong research background. The successful applicants will be mentored by Fred Samandari Director of the Wireless
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and academic backgrounds, who contribute complementary perspectives to our research. In our daily work we collaborate closely, exchange ideas, and make extensive use of our laboratory facilities
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international and most innovative employers in the region. With more than 6 000 employees from 100 different countries, we are helping to build tomorrow's world every day. Through top scientific research, we push
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 16 days ago
of North Carolinians and others whom we serve. We accomplish this by providing leadership and excellence in the interrelated areas of patient care, education and research. Patient care: We promote health and
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New Jersey’s academic health center, Rutgers Biomedical and Health Sciences (RBHS) takes an integrated approach to educating students, providing clinical care, and conducting research, all with the goal
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and integration of multimodal neuroimaging, behavioral and clinical data, and building large-scale deep learning models for multimodal neuroimaging datasets to construct predictive network models in