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Field
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Matter Physics / cond-mat-th , Condensed Matter Physics, Electronic Structure, Strongly Correlated Materials , Condensed Matter Theory , Strongly Correlated Materials , Superfluidity and superconductivity
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in computational research Comprehensive understanding of quantum mechanics and electronic structure theory is critical Experience with CFD (e.g., the use of OpenFoam or ALDFoam) and microkinetic
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simulation methods, decision theory, uncertainty quantification, machine learning. Applications and areas of key innovation Image analysis, computer graphics, autonomous and assisted driving, 3D scene analysis
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-traditional, e.g., event data) and network structures (for sensor networks). In this project, we will investigate Bayesian deep learning approaches to training models under uncertainty for several sensing
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relative to the university’s broader compensation structure. We are committed to offering competitive and flexible compensation packages to attract and retain top talent. Benefits Rutgers offers a
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structural and algorithmic graph theory. The purpose of the role is to contribute to the project “Algorithmic meta-classifications for graph containment”, working with Professor Matthew Johnson, Dr Barnaby
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Computer Engineering within the USC Viterbi School of Engineering. The ideal candidate will have an extensive background in one or more of the following areas: Information theory, structured statistics
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of [map ] Subject Area: Condensed Matter Physics / Condensed Matter Physics, Electronic Structure, Strongly Correlated Materials Appl Deadline: none (posted 2025/02/17) Position Description: Position
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variety of simulation and optimization techniques. Key areas of interest may include control theory, robust optimization, or distributed optimization. 2. The second candidate will focus on applied research
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
and astronomy to undergraduate and graduate students at UNC-CH. Training in physics is fundamental among the natural sciences because it provides understanding of the forces governing the structure