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Field
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: Experience in development, parametrisation, validation and application of atomistic machine-learning models (machine-learned interatomic potentials or models predicting atomic properties such as NMR parameters
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“Quantifying Efficacy and risks of solar radiation management (SRM) approaches using natural analogues”. The project will use novel machine learning-based methods to determine the climate response to a range of
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of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and adaptation of existing approaches for scientific applications
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National Aeronautics and Space Administration (NASA) | Huntsville, Alabama | United States | about 4 hours ago
to advance use of foundation models for Earth Science research and applications, including fine-tuning experiments Use of remote sensing, models, and/or machine learning to further our understanding
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in dynamical systems modeling (ODEs) and machine learning and very strong programming skills (Java, Python). A background in evolutionary genomics research is a strong plus, as is previous experience
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, the identification of predictive features, and the construction and validation of statistical or machine-learning-based models. The postdoctoral researcher will be responsible for: Developing a
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for the analysis of hyperspectral imaging data applied to pictorial layers, based on coupling physical radiative transfer models (two-flux and four-flux approaches) with machine learning methods. The researcher will
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studies of physics‑informed control of mobile manipulators, data collection from real and simulated machines, and model development and testing in simulated environments. The project offers close
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in dynamical systems modeling (ODEs) and machine learning and very strong programming skills (Java, Python). A background in evolutionary genomics research is a strong plus, as is previous experience
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focused on exploration and development of AI models of auditory perception, towards a broader goal of understanding how the brain predicts and learns from human communication sounds such as speech and music