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Field
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FieldComputer science » OtherEducation LevelPhD or equivalent Skills/Qualifications CANDIDATE ’S PROFILE The candidate should possess a PhD in machine learning or computer vision and have a strong publication
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present on expedient osseous tools. This methodological breakthrough relies on discriminant analysis of surface textural data (ISO 25178 and SSFA) acquired by confocal microscopy combined with image
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ways to reduce agricultural or food waste and introducing new food or material products in waste management systems. The ideal candidate will bring expertise in fermentation and non-chemical processing
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methods, signal processing techniques, and data analysis pipelines for novel X-ray imaging modalities, including ghost imaging, quantum-enhanced imaging, and other correlation-based methods. As part of a
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. The research requires experience in high vacuum systems, laser, molecular beams and laser desorption sources, charged particle beams and optics, velocity-map imaging detection, and the associated data processing
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engineering-based research applicable to cardiovascular disease as part of an extramurally funded project. The successful applicant will create computational models from medical imaging data and run simulations
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variety of relevant biological systems. We are looking for a candidate, who • Holds a Master’s degree and a PhD in physics, biophysics, physical chemistry or a related field • Has several years
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period of 12 months in the first instance, with potential to extend. The project involves advanced theory for new ultrafast imaging experiments and is funded by Engineering and Physical Sciences Research
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solar (thermal and PV) and geothermal energy with soil-based thermal storage to enable reliable 24/7 operation of adsorption desalination. We also develop low-cost sorption composites from locally
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include the feasibility, practicality and success evaluation of prototype implementations. The team you will be working with: Dr. Maxime Cordy Sylvain Kubler Dr. Mike Papadakis The position holder will be