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Field
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | about 1 month ago
that diffusion models are a fundamental divergence from traditional deep learning paradigms. This suggests that existing generalisation theories are insufficient and highlights the need for a bespoke, algorithm
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-guided machine learning. You are quick to assimilate information and capable of independent research. Experience in the pharmaceutical sector is an advantage You speak and write English fluently. You are
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working on diverse scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and
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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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interest in social science applications, and with strong competence in statistics and machine learning. The successful candidate will develop predictive models using machine learning and work alongside other
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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. Technical skills: Proficient in using E-views and Stata for econometric modeling. Advanced expertise in business intelligence, data analysis, data science, and machine learning tools. Capable of developing
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, etc.) Knows-how: • Design, implement, and evaluate machine learning and deep learning models, including multimodal architectures • Process, clean, and integrate heterogeneous datasets (electrical
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State University and work closely with I-CREWS researchers across the state. The position emphasizes developing, integrating, and applying modeling approaches-including machine learning (ML), hydrologic