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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 7 days 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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23 Jan 2026 Job Information Organisation/Company IMT Atlantique Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions
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to the development of a sustainable society. We are Sweden's largest mathematical department, with around 200 dedicated staff, including 65 PhD students . We are a joint department between Chalmers University
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closely related field PhD training covering topics such as computational modelling, numerical methods, statistical analysis, machine learning or data-driven analysis of complex systems Experience 0–3 years
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collaboration. Qualifications: Applicants must have a PhD in Robotics, Control Engineering, Machine Learning, AI, Mechanical or Electrical Engineering, or a closely related field. Strong focus on robot
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Certificates/Credentials/Licenses N/A Computer Skills MS Office and willingness to learn software used in the research lab Supervisory Responsibilities No Required operation of university owned vehicles No Does
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graphs and related structures, limit theorems, stochastic calculus and applications, for example in machine learning and mathematical statistics Participation in the scientific activities of the department
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https://pubs.acs.org/doi/full/10.1021/acssuschemeng.5c0419 The successful candidate will be able to: Work safely and independently in a laboratory setting Learn new techniques and protocols Plan and
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player and great collaborator Strong interest in interdisciplinary work at the interface between neurodegeneration, modeling, screening and machine learning Prior experience in iPSC modelling and CRISPR
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machine learning technologies in order to provide evidence-based decision support tools in near real time across a variety of thematic domains: disaster risk reduction, sustainable agri-food systems