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imaging at the single‑cell level. We welcome applications from recent PhD graduates interested in these or related areas, especially those with expertise in quantitative in-vivo and ex-vivo imaging
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Differential Imaging (CDI) to exploit the fundamental property of light coherence. The PhD will focus on two complementary approaches: 1) Enhancing CDI with machine learning: improve this technique using
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Intervention invites applications for a two‑year postdoctoral fellowship within the project AI‑Driven Medical Imaging. The position is expected to start on 1 September 2026 or as agreed. The fellowship is funded
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: • PhD in Biological Science, Physics, or a related field. • Minimum of four years of dedicated experience in wide-field and confocal microscopy. • Solid background in applying a wide range of imaging and
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conservators and imaging scientists to apply the developed methods to new artworks. Where to apply Website https://emploi.cnrs.fr/Offres/Doctorant/UMR8029-NACCHI-012/Default.aspx Requirements Research
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Infrastructure? No Offer Description Area of research: PHD Thesis Job description:PhD Position - Denoising Methods for Dual-Tracer PET Imaging Positron emission tomography (PET) is often used to diagnose
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. Activités The PhD will employ a combination of simulation, optical engineering, and experimental validation: • Imaging processing and control algorithms: Use and develop existing coronagraphic simulation
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factorization methods, the candidate will be positioned at the forefront of genetic data science. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/297729/phd-position-in
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: Project Scientist Salary range: The UC academic salary scales set the minimum pay determined by rank and step at appointment. See the following table for the current salary scale for this position: https
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | about 15 hours ago
of the proposed research subject : A state of the art, bibliography and scientific references are available at the following URL: https://vincentgaudilliere.github.io/files