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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 8 days ago
. These one- to three-year fellowships are competitive and are designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology
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the start date. Job description This opportunity is positioned at the heart of a collaboration between Aarhus University, EPFL and DTU to understand microbiological mechanisms underpinning the Greenland Ice
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, education and faculty recruitment. Read more: https://wasp-sweden.org/ Qualifications To be appointed under the postdoctoral agreement, the postdoctoral fellow is required to have completed a doctoral degree
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funded 2 year postdoctoral position starting 1 January, 2027 or as soon as possible thereafter. Project overview The goal of this project is to advance the understanding of failure mechanisms in optical
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-quality cryo-ET data. The candidate will apply these workflows to challenging questions in the structural cell biology of the assembly of a unique machinery involed in bacterial motility and cell-autonomous
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closely related quantitative field. A solid foundation in Computer Science with a deep understanding of Artificial Intelligence and related algorithms. A strong interest in or prior experience with medical
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particularly abundant in barrier epithelia where they protect against opportunistic bacterial infection. AMPs are the major mechanism for antibacterial immune defense in adult D. melanogaster and other insects
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ground, marine, and air vehicles that move goods and people. The research will involve primarily modeling, simulation, and analysis with a possibility for some experimental model validation, but you will
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transportation professionals Premier office and research facilities and equipment Accredited laboratories and safety proving ground Outstanding research support services Hybrid work environment/telecommuting
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, or a related field. Solid research background and practical experience in one or more of the following areas: Reinforcement Learning / Deep Reinforcement Learning Fine-tuning and Application of Large