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of the land ice contribution to sea level rise until 2300 with machine learning. You will develop probabilistic machine learning “emulators” of multiple ice sheet and glacier models, based on large ensembles
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methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or
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developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and applications of AI. Proficiency in Python, leading ML frameworks
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within a cross-functional team, including software developers, electrical and mechanical engineers. Experience and strong understanding of machine learning algorithms, mathematical modelling, and
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will analyse quantitative imaging data from a variety of sources, including Spatial Transcriptomics and Multiplex Immunofluorescence platforms, for validation and calibration of mathematical models. You
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 15 hours ago
process modeling) and laboratory wave tank experiments to explore turbulent seawater dynamics adjacent to ice. Opportunities may also exist to participate in planned field campaigns in Greenland
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Vision Group (PVG) at McGill University and Mila—Quebec Artificial Intelligence Institute, led by Prof. Tal Arbel, seeks Postdoctoral Fellows to advance causal-temporal and probabilistic modeling, 3D
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, and you will join the research group led by Prof. Christa Cuchiero to work at the intersection of Mathematical Finance, Stochastic Analysis and Machine Learning. The research areas cover a wide range of
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consortium. As a formal qualification, you must hold a PhD degree (or equivalent). Other qualifications and competences include: Experience with mathematical modelling Experience with toxicology Experience