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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 2 months ago
retrieval algorithm development with focus on using the polarimetric signals, the new FIR or sub-mm bands, and/or the ML/AI approach; (3) ML/AI application on system/pattern tracking on satellite images
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new AI algorithms for science. Our group also welcomes new research directions, and collaboration across different groups at the laboratory and beyond. We publish in peer-reviewed journals and
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increasingly shapes biomedical research and healthcare decision-making, we also value candidates who can help students critically understand how algorithmic systems affect equity, access, bias, and real-world
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numerical methods for quantum many-body systems, (iii) correlated electronic structure theory, (iv) tensor network algorithms. Applications should be submitted by March 1, 2026 for full consideration
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the project activities. – Assist in the dissemination of project results through presentations, publications, and stakeholder engagement. Development of algorithms (80%) – Conduct a comprehensive review of the
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algorithms, computational complexity theory, and information theory Relevant coursework and experience in spiking neural networks, and statistics A strong electronics background, including experience in
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Tenure-track Assistant and Associate Professorship positions in Algorithms at the Department of M...
We seek candidates to join the Algorithms Section. The focus of the call is both to expand at the new Vejle campus and to strengthen the Odense campus. We have several openings, and successful
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contextualized by existing expertise in existing methods and state-of-the-art in the field. The position includes algorithm design, software implementation, and validation on experimental datasets. You will
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to sign the contract. More information is available on: https://www.dges.gov.pt/pt/pagina/reconhecimento . Workplan and the objectives to achieve: This work aims to develop a numerical approach based on
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emphasis on questions grounded in data that are generated by human activity, including computational social science (e.g., algorithmic accountability and the interplay of data science with policy, law, and