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sciences discipline such as oceanography, remote sensing, or meteorology. • Excellent data management and computing skills. • The ability to engage with and work with a wide variety of international
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into many novel aspects of developing offshore digital twins, such as sensing, data handling, physics and AI modelling, software development, as well as experiments at the Cranfield Ocean Systems
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1st August 2026 Languages English English English PhD Research Fellow in Vision-Language Models in Remote Sensing Apply for this job See advertisement About the position A fixed-term 100% position
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Environmental and Remote Sensing Center (NERSC, project leaders), and researchers from ETH-Zürich in Switzerland are international collab-orators. The primary research tool in the project is the Norwegian Earth
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a summary of the applicant's PhD research and career goals and outlines how an Erb Institute post-doctoral fellowship will advance their sustainability research career. Current doctoral candidates
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team of researchers, postdocs and PhD students working on intelligent observing systems using machine learning and data assimilation methods in the ACTIVATE project. UiO/ Anders Lien via Unsplash UiO
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Inverse Methods and Ionospheric Modelling Research Fellow - School of Engineering - 106995 - Grade 7
empirical modelling, D-Region ionospheric remote sensing, or ionospheric radio propagation. This 3-year post will provide the candidate with the opportunity to fully engage with the research group, with work
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addition to research activities, the fellow will mentor PhD and Master's students in the IMERSE Lab on topics related to autonomous robotics and mechanically intelligent tool design, including strategies for ex vivo and
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data analysis or remote sensing Have familiarity with planetary science datasets and/or space mission data Demonstrate analytical and critical thinking, as well as problem-solving Be able to work
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fellow will be part of a growing team of researchers, postdocs and PhD students working on intelligent observing systems using machine learning and data assimilation methods in the ACTIVATE project. UiO