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—particularly in topology, geometry, or related fields—and would also take genuine interest in thinking about how formal models can inform sociological theories regarding the structure and process of science. The
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across science and technology studies, political sociology, computer science, and engineering. The postdoc’s duties will also include some teaching and supervision. Further information on the Department is
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. About the Role: The successful candidate will be expected to: Carry out high-quality research under the supervision of our experienced faculty members. Participate in the development of theories, and
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background and some understanding of game theory will be more important than particular disciplinary focus. The positions will also have close ties to the US NSF Center for Analysis and Prediction of Pandemic
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of biological and statistical physics. Candidates for the position must have a PhD in physics or a related discipline, preferably with expertise in stochastic processes, nonlinear dynamics, and control theory
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in communication theory, signal processing, and wireless communications Proven track record of publications in relevant IEEE journals and conferences Extensive experience in physical layer algorithm
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candidate will hold a PhD in theoretical or mathematical physics, with knowledge of random matrix theory and/or its applications to theory of disordered/chaotic systems, and with some numerical skills
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at this Level Education/Training: PhD (theoretical nuclear/high-energy physics, quantum information science, lattice gauge theories, quantum many-body dynamics) Experience: Preferred--computational experience in
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prior experience conducting social science experiments (lab, lab-in-the-field, online, and/or field). Researchers working at the intersection of experiments and other methods (theory, empirical) as well
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privacy constraints. About the Project The central aim of this project is to pioneer statistical methods for high-dimensional diffusion processes under privacy constraints. While stochastic differential