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Field
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probabilistic ensemble techniques to improve uncertainty representation in climate simulations. Apply and validate Monte Carlo methods (e.g., importance sampling and other rare-event estimation methods
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are seeking highly qualified applications to explore visual and LiDAR based navigation methods in the IDEAS lab (https://ouideas.github.io/). The expected responsibilities for this position are to assist with
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background in Statistical Mechanics and to mostly focus on analytical work and the development of novel quantitative methods for active matter, and to a lesser extent, on computer simulations of active fluids
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of de-anonymization, including researching current de-anonymization strategies Investigate synthetic data generation methods and their utility Deploying and benchmarking the above-mentioned methods
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evaluations of AI systems across diverse cultural contexts Collaborate with computer scientists and anthropologists to develop new methods for pluralistic alignment Publish high-impact research in top venues in
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. Ready to be part of our team? Let’s shape the future together! About the team: Our group develops and applies quantum mechanics-based methods for the description of condensed matter systems, including
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responsible for conducting highly specialized and technical research. The individual will be primarily engaged in the use of computational methods for compound design, including but not limited to molecular
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evaluations of AI systems across diverse cultural contexts Collaborate with computer scientists and anthropologists to develop new methods for pluralistic alignment Publish high-impact research in top venues in
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technologies. The Pritykin lab (http://pritykinlab.princeton.edu ) develops computational methods for design and analysis of high-throughput functional genomic assays and perturbations, with a focus on multi
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well as the profitability and cost-effectiveness of agricultural production within the agrivoltaics systems. This Postdoctoral Associate will be part of the Cornell University team responsible for conducting mixed-methods