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scientific achievements documented by numerous publications and citations in the field of environmental engineering and safety analyses of nuclear reactors. In particular, in the area of probabilistic methods
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welcome: Real-time or streaming pipelines Control / robotics / feedback systems Optical systems / optical tweezers / laser-based manipulation Probabilistic modelling / tracking Software in the loop (SiL
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be in charge of finishing ongoing projects in the group, involving the development of probabilistic methods for the identification of non-coding cancer driver elements and of the tumor mode of growth
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to Reason (Inactive), Analytical Thinking, Big Data Processing, Bioinformatics, Communication, Complex Data Analysis, Data Management, Group Problem Solving, Laboratory Processes, Probabilistic Modeling
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the requisite experience. A2 Knowledge of mathematical and statistical methodologies including several of: Statistical modelling and inference, Bayesian statistics and probabilistic modelling, Inverse problems
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statistical subject, where probabilistic models are combined with computational algorithms to solve challenging complex problems, as well as a statistical view of machine learning which clearly integrates
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applicant has a strong background in bioinformatics and/or probabilistic machine learning, as well as experience in omics data analysis, and possesses solid English-language skills. Experience with
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: Experience with probabilistic graphical models, time series analysis, or deep learning Familiarity with reproducible research practices and open-source collaboration Interest in interdisciplinary applications
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 hours ago
to approximate expensive forward and adjoint simulations while preserving underlying physics. Uncertainty-aware inference: combining physics-informed learning for regularization with probabilistic generative
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communities. 3) Probabilistic Modeling Toward Strong AI: We are seeking candidates whose expertise is grounded in a sophisticated understanding of how probabilistic modeling plays a crucial role in knowledge