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-processing Mentor and work closely with multiple Ph.D. and M.S. students as well as four principal investigators from different disciplines. Frequent oral and written reporting will be required. Outstanding
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, ill-posed nonlinear inverse problems Numerical optimization techniques Machine learning Strong programming skills in Matlab and/or Python are required. These should be documented, for example through a
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one of the following areas is required: Numerical methods for large-scale, ill-posed nonlinear inverse problems Numerical optimization techniques Machine learning Strong programming skills in Matlab and
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enhance modern microscopic imaging techniques. Designing setting up and executing the experiments Developing relevant algorithms for experimental data post-processing Mentor and work closely with multiple
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of multiple modes of buses, AMOD vehicles, private cars, cyclists, and pedestrians, and optimizes multicriteria involving passenger delays, traffic efficiency, environmental impact, and equity, among others
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vibration analysis techniques Strong understanding of drilling automation systems and modern control theories, including multivariable control, learning-based control, nonlinear dynamics, convex optimization
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. Required Experience: 5-6 years of knowledge of both traditional and evolving structural design, analyses and optimization methods; VB scripting in Catia GSD, Ansys Python and APDL; linear and nonlinear FEA
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, including NSF, NIH, DOE, DOD, and AFOSR, and include complex multi-disciplinary, multi-institutional, and multi-PI awards. Sponsored project expenditures run about $20M/year. Instruction occurs at multiple
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to dissociate memories. Finally, it proposes to use this information to come up with novel strategies to make machines unlearn better, more efficiently, and more safely. The potential impact of this project is
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, biophysics, condensed matter physics, controlled fusion & plasma physics, elementary particle physics, nanoscience, and nonlinear dynamics. Research activity occurs both on-campus and in multiple off-campus