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. Work will emphasize the development and analysis of advanced methods in areas such as sparse signal recovery, compressed sensing, and statistical estimation, with a particular focus on their role in
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University of California, San Diego | Estancia de San Diego, Guanajuato | Mexico | about 6 hours ago
-order predictions together with heterogeneous observational data. • Integrate data driven methods into a framework for uncertainty-aware ocean state estimation. • Apply and validate the framework using
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field robotics and tests its methods on robots in real environments. We are now looking for a postdoctoral researcher to work on localisation and mapping for robots in GPS-denied environments, where dust
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enabling process monitoring, learning, and policy training while ensuring safety and efficiency. For this, we develop novel learning-based control methods with practical applicability and theoretical
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, our actions together, large and small, contribute to a better world. We look forward to receiving your application! Work assignments You will join the research project Countering Bias in AI Methods in
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field robotics and tests its methods on robots in real environments. We are now looking for a postdoctoral researcher to develop sensing and autonomy for drones that inspect infrastructure and terrain in
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Develop methods for animal and environment reconstruction Develop methods for behavior understanding from multi-view video and audio recordings Advance multimodal large language models for fine-grained
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learning emulators to represent forward operators for fire observations for atmospheric data assimilation Implement ensemble methods in the fire emissions estimation and estimate uncertainties Utilize
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
learning emulators to represent forward operators for fire observations for atmospheric data assimilation Implement ensemble methods in the fire emissions estimation and estimate uncertainties Utilize
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, oscillation, response-time, and evidence-accumulation phenomena. Implement simulation, parameter-estimation, and model-comparison methods in Python, MATLAB , R, or related computational environments