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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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. What you should have: A 1st degree in physics or engineering. An interest in optics, some ability in computer programming A desire to learn new skills in complementary disciplines. You will work jointly
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, functional, and molecular imaging data. This multimodal technology is optimized for preclinical research in oncology, neurobiology, embryology, and cardiology. Its non-invasive nature allows longitudinal
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15 Jan 2026 Job Information Organisation/Company CNRS Department Institut Langevin Research Field Engineering Physics Technology Researcher Profile Recognised Researcher (R2) Application Deadline 4
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Assistant Research Professor (Research Track), Radiology and Imaging Sciences Posting Number req24876 Department Radiology & Imaging Sci Dept Department Website Link https://radiology.medicine.arizona.edu
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26 Jan 2026 Job Information Organisation/Company CNRS Department NAnomédecine, Biologie extracellulaire, Intégratome et Innovations Research Field Pharmacological sciences Engineering Technology
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to explore include mono-chromatic and multi-chromatic illumination, structured illumination, and telecentric illumination distortions. Computer based image analysis approaches will be developed, including
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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FieldMathematicsEducation LevelMaster Degree or equivalent Skills/Qualifications Engineering degree or Master’s in statistics and/or computer science/applied mathematics LanguagesFRENCHLevelBasic LanguagesENGLISHLevelGood
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communication, organization, and interpersonal skills Attention to detail and accuracy Knowledge of digital image technology, photographic processes and ability to evaluate analog and digital image quality