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control, numerical analysis and scientific computing risk-averse and fractional models, digital twins and data-assimilating models, machine learning and neural networks (including operator learning and
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University of California, Los Angeles | Los Angeles, California | United States | about 15 hours ago
complex datasets, including model fitting, statistical testing, and data visualization in R and/or Python. Ability to work closely with experimentalists to ensure analyses complement experimental goals and
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cereals, field trials, plant/crop modelling, genomics, genetics, crop ecophysiology, and agronomy. Evidence of contribution to successful external grant applications. Experience liaising and collaborating
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We are seeking creative and energetic candidates with strong experience in multimodal machine learning and human behavior analysis and modeling for a one-year Postdoctoral position. Using recent
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models for cross-country and global monitoring, including linkages, spillovers, and macro-financial risks Applications of large language models and text-as-data methods to extract signals from news, policy
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: Conceptualizing suitable empirical methodologies and models Collecting, managing, and structuring quantitative datasets Conducting statistical analyses of complex datasets and interpreting results Report writing
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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
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for isolating milk proteins and characterizing their structure. We are particularly interested in exploring the functional properties of these proteins using mammalian cell line models. Position Overview The Food
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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
; - survey and analyze the state of the art in emerging wireless networks, including simulation aspects; - collaborate in the preparation of technical reports on the algorithms, mechanisms, models
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climate change - Computer vision, e.g., colour vision, human colour vision, colour appearance models, etc. - AI technology, e.g. statistical learning, neural network learning, deep and transfer learning