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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 4 days ago
-series. Experience exploring machine learning and deep learning techniques for geospatial applications is highly desirable to effectively engage with Earth observation foundation models. Technical
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the postdoctoral program where together with you we will prepare a career plan, which contains the skills and knowledge you will acquire Open and inclusive working environment with committed colleagues Favorable
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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applications for a post-doctoral research position under the supervision of Dr. Chris Smith Home | Chris Smith . The lab— in the Evolution, Ecology, and Behavior section—investigates machine learning approaches
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, machine learning, data-driven dynamics, stability/perturbation theory/rigorous numerics. Possible applications of the new theory include the analysis of spatiotemporal data arising from weather and climate
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
world. Position Summary This postdoctoral researcher will work under the supervision of Prof. Tianlong Chen in the UNITES Lab. The focus of this position will include, but is not limited to, multimodal learning, AI
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UiO/Anders Lien 16th October 2026 Languages English English English PhD Research Fellow in Machine Learning and Statistics Apply for this job See advertisement About the position Integreat
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Posting Details Position Details Title Postdoctoral Fellow in Medical and Molecular Genetics Specific Title Appointment Type Postdoctoral Fellow Department IUSM - Medical & Molecular Genetics Campus
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/nanoplastics, and other environmental compounds to assess their potential impacts on human health and the environment using machine learning (ML), deep learning (DL), and big data analytics. His lab is
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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the