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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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Fellow (EB1)1, available in https://dre.pt/application/conteudo/127238533 and in accordance with the consolidated version with the changes resulting from the update, approved on December 10, 2025 by
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on methods to improve understanding of how machine learning algorithms work. Workplan: Literature review Design of an approach for the selected problem Empirical evaluation of the proposed approach Writing
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and resilience across heterogeneous computational resources while addressing workflow requirements for scientific applications. Validate distributed intelligence algorithms at scale on ORNL's
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requirements: Experience using deep-learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating on scientific projects. Publications on deep-learning topics. 4. Work Plan
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be duly proven at the time of hiring. 2; 3. Preferred requirements: Experience using Machine Learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating
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Government of Canada | Government of Canada Ottawa and Gatineau offices, Ontario | Canada | about 1 month ago
and proceedings, presentations at top conferences, and widely recognized contributions to software projects. • Designing, implementing and testing new algorithms or applications in one or more
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are filled. An earned doctorate is required by the start of the appointment, and a background check must be completed prior to beginning employment. Application materials should be submitted to http
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. Application materials should be submitted to https://www.mathjobs.org/ . The Georgia Tech official website for applications is https://hr.gatech.edu/careers . Some candidates who do not apply there at first
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a PhD student, you will develop state-of-the-art learning and inference methods to detect and characterize anomalous radio behavior and to design algorithms that remain reliable under practical