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at finale.seas.harvard.edu and our group’s webpage https://dtak.github.io/ We work on probabilistic models, reinforcement learning, and interpretability + human factors. Basic Qualifications Candidates are required to have
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and responsible space exploration, with planetary protection at the core of its efforts. You are encouraged to visit the ESA website: https://www.esa.int/ Field(s) of activity/research
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%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model
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using modern Bayesian computing and probabilistic modelling tools such as Stan, TMB, INLA, PyMC. Experience applying reproducible research and team science tools and workflows (e.g. Git/Github
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join the Tang Lab. The Tang Lab (https://tangxinlab.org/ ) develops explainable, autonomous, and multimodal artificial intelligence (AI) systems to advance biological discovery. Our research integrates
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loadings. It combines experimental data, finite element simulations, and probabilistic models. Results depicted on Fig. 1 and Fig. 2 show high extrapolation and interpolation capabilities of the obtained
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interpret the architecture to local field potential data recorded in humans who have seen a vast number of images from the CoCo-database (https://cocodataset.org ); and apply and interpret the architecture
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probabilistic behavioral models for verification, performance evaluation, and optimization using model-checking techniques, ultimately bridging static system design and dynamic operational analysis. We offer
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian Multi-Energy System & Grids Team is looking for a Research Fellow in
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Deviations” (TOMABOLD), funded by the Research Council of Norway. The PhD position will focus on the large deviation analysis of probabilistic models, and associated problems in PDE, with emphasis