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Biomedical Data Science Postdoc Appointment Term: 2 years (can be exended) Appointment Start Date: December 1, 2025 (Flexible) Group or Departmental Website: http://med.stanford.edu/summerhanlab.html (link is
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international conferences or journals, especially publications in the area of Air Traffic Management Knowledge of optimization techniques Knowledge in the area of design and analysis of algorithms Great emphasis
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee Our department is looking for a Research
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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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contribute to the development of fundamental aspects of computer science (models, languages, methodologies, algorithms) and to address conceptual, technological, and societal challenges. The LIG 22 research
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period of 12 months, possibly renewable up to a maximum of 36 months, scheduled to start on March 2026. 2. WORK PLAN AND WORKPLACE: The project will investigate the developed algorithms and methods
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will remain Open Until Filled. The selected candidate will be hired as an R&D Engineer III. Follow us here: https://www.linkedin.com/company/digital-ag-innovation-lab . Example of Duties • Leads complex
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 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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and resilience across heterogeneous computational resources while addressing workflow requirements for scientific applications. Validate distributed intelligence algorithms at scale on ORNL's