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and/or image processing * Strong proficiency in programming with Python and Matlab * Good command of English Knowledge of solid mechanics would be an asset, but is not essential. Website for additional
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interacts with the Quantitative Imaging Platform of Villefranche (PIQv; https://sites.google.com/view/piqv ), which oversees the operation of the tools that the team develops. Those tools include imaging
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sciences provides data management and AI based Image analysis, an internal experimental group, and an internal simulation-sciences group (You+Supervisor+support by lab). These collaborations enable
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play a key role in the integration and analysis of omics data collected within the groundbreaking TREAT EARLIER trial. This trial is the first preventive study in the field of rheumatology globally and
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electrophysiology in brain slices and cell cultures. Performs behavioral tests in mice. Performs in vivo 2p imaging in head-fixed mice. Compiles and analyzes data with appropriate conclusions and recommendations in
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disease diagnostics. Responsibilities: Design, prototype, and test microfluidic systems for point-of-care biosensing applications Develop implantable or wearable devices for real-time in vivo diagnostics
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. Collaborate on multidisciplinary projects involving high-throughput phenotyping platforms. Apply machine learning and deep learning techniques to improve image processing and trait prediction. Analyze large
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responsibilities will be to: Photonic System Innovation: Craft state-of-the-art instruments and components for commercialisation across optical communications, hyperspectral imaging, and astronomical applications
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well as an active engagement in the 3RTG activities. Requirements: successfully completed university degree (Master's, Diploma or equivalent) and relevant PhD in Computer Science, Computer Engineering, or related
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, bright individuals with a diploma or master’s degree in bioinformatics, biochemistry, biology, molecular/translational medicine or related subjects and a PhD in life and computer sciences. Previous