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to the development of scalable, explainable, and uncertainty-aware AI methods that enhance model robustness, reliability, and scientific discovery. Publish research findings in high-impact journals and present results
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 hour ago
. Description: Aerosols have a profound impact on climate, influencing the energy budget through their direct and indirect effects. These effects represent an important source of uncertainty in greenhouse gas
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theoretical foundation. We are looking for a candidate excited by the challenge of rethinking data systems in the face of modern constraints such as optimization under uncertainty, constrained query evaluation
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experiments in gelatine (https://doi.org/10.5194/egusphere-egu25-9211). This model will be tested with analogue experiment results from the MAGMA Lab, run by other members of the project team. The second
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- Integration, demonstration and proof by digital twins: M10-M16 Where to apply Website https://www.imt-mines-ales.fr/ Requirements Research FieldEngineering » Systems engineeringEducation LevelPhD or equivalent
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using open-source data, assess the reliability, transparency, and applicability of these models, and validate them through applicable use-cases. Where to apply Website https://www.academictransfer.com/en
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formulations into existing land-surface or vegetation model platforms, conduct uncertainty and sensitivity analyses, and compare against publicly available sap-flow and environmental data. 6) Mentoring
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Double Beta-decay (@NDB)” (https://tarheels.live/atndb/). The @NDB Hub involves theoretical physicists and statisticians at the University of North Carolina, Colorado State University, Los Alamos National
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and learning adaptation to uncertainty. You will use fMRI and neurostimulatory techniques (ultrasound neurostimulation and/or transcranial magnetic stimulation) to test the causal role of targeted
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high uncertainties about which ReX strategies to adopt at the product, sub-assembly, and component levels. Prior research in the group has focused on AI-supported product model identification, pose