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, Python or R Experience in manipulating and analyzing large observation data or model outputs Working knowledge of terrestrial biogeochemistry and nutrient-cycling processes Strong communication skills
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collection, and statistical analysis. Proficiency in statistical software (e.g., R, SAS, STATA) and data management techniques. Demonstrated experience in analyzing large epidemiological datasets. Excellent
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, higher-order network methods and large-scale language-model training on a knowledge graph of scientific publications, patents and related innovation data. The appointed researcher will lead DII's
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University of California, Los Angeles | Los Angeles, California | United States | about 21 hours ago
clinical data. A major focus of the position will be identifying genomic and immunologic features associated with variation in immune responses and clinically relevant phenotypes. A Ph.D. or equivalent
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19 Sep 2026 Job Information Organisation/Company CNRS Department Géosciences Environnement Toulouse Research Field Chemistry Biological sciences Pharmacological sciences Researcher Profile First
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on probability, partial differential equations, large deviations, stochastic analysis, optimisation and machine learning. The successful candidate will contribute to the activities of the Machine Learning & Data
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science, cybersecurity and logistics & supply chain, committed to collaborating on large research projects with an emphasis on additive manufacturing. One of the focus areas of CAAAM is Data Science
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 4 hours ago
variability and natural disturbances. The research will integrate NASA Earth observations, spaceborne LiDAR measurements, hydroclimatic information, and Earth-observation foundation models to move beyond static
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visualizing large-scale, complex data. For the vacant position, we particularly search for a colleague experienced in computational text and/or image analysis and processing platform data. Your future tasks
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, Python or R Experience in manipulating and analyzing large observation data or model outputs Working knowledge of terrestrial biogeochemistry and nutrient-cycling processes Strong communication skills