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Program. The successful applicant will commit to a demanding clinical schedule, providing supervised clinical ethics consultation, on-call coverage, education throughout the UCLA Health system, assistance
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in network science. NetSI faculty and researchers are working to advance the understanding of complex systems through the lens of networks, and to apply this knowledge to solve real-world problems in a
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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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curious to deliver work that matters, your journey starts here! Innovation. Interdisciplinary collaboration. Complex problem solving. In Carnegie Mellon University’s Department of Mechanical Engineering
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an understanding of the world’s complex, interlocking systems. We believe that access to nutritious food and a healthy environment is a fundamental human right. We believe that ensuring a prosperous global future
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complex instruments and run simulations to accelerate discovery. This involves navigating vast parameter spaces, identifying rare or transient phenomena, and dramatically optimizing the use of precious
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concerns: We believe that achieving next-generation scientific breakthroughs requires an understanding of the world’s complex, interlocking systems. We believe that access to nutritious food and a healthy
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| Materials Science and Engineering This position will focus on the molecular beam epitaxy (MBE) growth and characterization of thin films and heterostructures of complex oxides. The successful candidate will
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 7 hours ago
equivalent doctorate (e.g., Sc.D., M.D.) Departmental Preferred Experience, Skills, Training/Education: Ph.D. in Political Science, Economics, Public Policy, Geography, or a closely related Computational or
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skills and curiosity about complex systems. Position Overview You will design and implement new computational and statistical models to reverse-engineer causal networks from noisy, high-dimensional, multi