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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 months ago
of radiance data from new hyperspectral infrared instruments such as IASI-NG, MTG-IRS Enhancement of CrIS radiance assimilation algorithm are highly encouraged. - Use machine learning methods to cope with model
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 2 months ago
Lidar and the Roscoe upper troposphere/lower stratosphere lidar). Additional projects include the development of machine learning and advanced data processing algorithms, and participation in upcoming
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, and inline interpretation. The work includes validating algorithms on a lab-scale sorting setup and linking spectroscopic outputs to recyclability scoring and Digital Product Passport data flows. Your
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algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we are looking for The following requirements are mandatory: A doctoral
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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development, networking, administrative and technical support functions, along with good employment conditions. More information about the department is available at: https://www.umu.se/en/department
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 months ago
to develop/translate model algorithms and develop new model code in Fortran, d) software skills needed to work with multiple observed and model datasets, e) a strong desire/motivation to develop scientific
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imaging, computer vision, and predictive modelling. The postdoc will further develop an existing rumen‑fill scoring algorithm into a functional prototype and pilot the technology for longitudinal monitoring
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algorithms, architectures, and learning strategies that fundamentally challenge existing resource constraints in large-scale AI systems. Prototype, implement, and rigorously evaluate complex machine learning