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some of the following areas: molecular dynamics, Monte Carlo simulations, statistical mechanics, computer programming (e.g., C++, Python), polymer theory, molecular modeling (e.g., of proteins, nucleic
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experiments, including setup of detectors, electronics, and data acquisition systems. Experience analyzing complex experimental and Monte-Carlo simulated data to optimize the analysis of raw detector data in
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analysis related to sampling, optimisation and learning problems in high dimensions. Examples of current research topics include convergence analysis of Markov processes, efficient Monte Carlo methods, large
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clinical trials by harnessing external historical controls or another type of auxiliary information from existing clinical trials or real-world data. The Postdoc Associate will use analytic and Monte Carlo
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technical and scientific development of these facilities. The candidate will have the opportunity to work with data and Monte Carlo simulations from both projects. They may also contribute to the development
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financial dynamics Apply machine learning and Monte Carlo techniques to simulate complex decision scenarios Contribute to a growing, interdisciplinary field that redefines biodiversity through the lens
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by time of appointment. The proposed research will leverage multiple computational many-body techniques (including classical and quantum Monte Carlo, molecular dynamics, and ab initio methods) and
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: Experience in radiological risk assessment Experience in biokinetic model development Experience with Monte Carlo radiation transport software and applications Experience with numerical computing using
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, LROC, ROC, Image perception, mathematical/computational observer models. Both experimental and computational positions available. Skills preferred include any of these: Monte Carlo simulations, benchtop
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Ph.D. in Physics or a closely related field is required. Candidates with a strong background in quantum many-body theory and experiences in quantum Monte Carlo and tensor network methods are encouraged