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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 5 days ago
the surface properties of the Asal-Ghoubbet rift by massive inversion of the Hapke model on Pleiades multiangular images. Remote Sensing of Environment 322, 114691. https://doi.org/10.1016/j.rse
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longitudinal consistency and data quality, including parameter tracking, protocol versioning, and rigorous QC frameworks Implementing, validating, and extending image-processing and analysis pipelines
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engagement•Interest in translating scientific insights into policy and practice Selection process How to Apply Please submit: A motivation letter explaining your interest in this PhD and how your
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UiO/Anders Lien 9th February 2026 Languages English English English Join a vibrant team at the University of Oslo as a PhD Research Fellow in Deep Learning for geoscience imaging! PhD Research
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to an expert committee from the University of Nottingham as part of a competitive process to secure the funding. How to apply: Fill out this form: https://forms.office.com/e/SxNLR1qbBE?origin=lprLink , answer
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Dortmund, we invite applications for the Research Group Analysis of Microscopic BIOMedical Images (AMBIOM): PhD Candidate (m/f/d) The position is part of the DFG-funded project: ComplexEye - a 96/384-well
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at http://www.icgi.no and http://www.domore.no. The position is based in the Digital Signal Processing and Image Analysis group (DSB), Section for Machine Learning, at IFI. DSB has seven full-time and five
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processing for wireless communication systems including satellite communications and radar systems and is currently expanding its research activities in exploring several emerging use cases of next generation
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salary, which is determined by the number of years post PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic
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into operational decision‑support tools for farmers, in close collaboration with an industry partner. The project focuses on automated rumen‑fill assessment using 3D imaging, computer vision, and predictive