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Field
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. The postdoctoral scholar will contribute to an exciting research program within the Foy Lab (www.foylab.xyz/) https://foylab.xyz/ , developing machine learning, computational, and mathematical models for improving
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equations, numerical analysis, mathematicalmodels in biomedicine, biostatistics, machine learning, statistical learning, multivariate statistics, time series,mathematical modelling, generalized linear models
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 2 months ago
institute of the Rennes University (IRMAR). The team expertise encompasses mathematical (stochastic) and numerical modelling of ocean flows, observational and physical oceanography, data assimilation and
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Engineering and production optimization Documented mathematical, analytical and problem-solving skills. Good knowledge of statistics, AI/ML, and mathematical modeling and programming. Good knowledge in academic
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the UCD School of Mathematics and Statistics. The successful candidate will lead research focused on developing a scalable, AI-driven quality control (QC) system for synoptic and climate observation data
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development About You You will hold a Ph.D/D.Phil in a quantitative or theoretical discipline (e.g. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely
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Position Description Northeastern University Department of Biology A Postdoctoral Researcher role is available in Mathematical Biology at Northeastern University working with Assistant Professor
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Postdoctoral Fellow positions as part of the NSF AI Institute for Human-AI Cooperation (NSF HAIC) (https://haicinstitute.org/), a $21.5 million multi-institutional research initiative across the University
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have a solid understanding of the different types of superstring models and associated supergravity theories. Knowledge of mathematical concepts useful for the project (differential geometry, exceptional
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animal models and human participants. Particular emphasis will be placed on identifying translational motor features that can be robustly and consistently quantified across species, including movement