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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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at conferences, and stakeholder engagement sessions. Required Qualifications: A Ph.D. in Climate Science, Hydrology, Environmental Science, or a related field. Experience in machine learning or AI applications in
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effective teaching experience who can teach online (Canvas), on campus, and/or at off campus locations and who can teach introductory and advanced courses in women’s studies, gender, masculinities
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Inria, the French national research institute for the digital sciences | Bordeaux, Aquitaine | France | about 2 months ago
and loosely supervised learning, they could provide more efficient methods useful for machine learning and artificial intelligence domains. Moreover, such sensorimotor models will be use as tools
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, aiming to enable faster, cheaper, and more robust production through machine learning, multi-scale modelling, and advanced process simulation. The successful candidate will be based at the Bristol
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machine learning for time series, geospatial data or dynamic models; ideally experience with deep learning frameworks (e.g., PyTorch). Strong analytical and conceptual skills for designing and interpreting
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equations, statistics, game theory, machine learning Deadline for submitting applications: 20 February 2026 Required documents All documents must be submitted via the online platform https://jobs.uw.edu.pl/en
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for collaboration. You will also have the opportunity to develop your own research project aligned to the interests of the MND group. This could include new machine learning models or exploring a particular aspect of
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characterize the spatio-temporal contexts that favor crises. • Development of advanced predictive models (multivariate approaches, machine learning) combining event data, snow and weather data, and remote
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that challenge prevailing assumptions, employ cutting-edge technologies, or integrate machine learning with neurobiological data are especially welcomed. Projects focusing primarily on animal models with