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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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preferred • Experience in image processing and analysis also preferred • The candidate will report directly to the Principal Investigator and will successfully carry out experiments and analyze the data
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: Carrying out research in the predefined areas Implementing prototype tools Managing and contributing to national and international research projects Preparing project deliverables Disseminating results
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and coatings. You will be given the opportunity to work collaboratively with a motivated team of early career researchers and PhDs on optical low-adhesion coatings. Within the team, you will be given
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academic or industry leadership roles. Your profile Applicants should hold a PhD in Computer Science, Electrical Engineering, Computer Engineering, Telecommunications, or a similar field, with a strong
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embedded in nanoscopic droplets of superfluid helium. Specific projects include, but are not limited to: 1) Time-resolved imaging of cation-molecule complex formation 2) Time-resolved stereodynamics
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/PhD in a mathematical branch of study Solid knowledge of one of the following fields: mathematical methods, analysis and numerics in imaging, inverse problems or data sciences Ability to integrate
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chimeric mouse models of AD. * Perform molecular, cellular and imaging assays (sample processing, immunofluorescence, microscopy, etc). * Analyze and interpret data; prepare reports, manuscripts, and
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) department at Telecom Paris. Reinforcement learning (RL) has emerged as a useful paradigm for training agents to perform complex tasks. Model-based RL (MBRL), in particular, promises greater sample efficiency
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microscopy workflows to study battery materials and interfaces. Work with new approaches for preparing and transferring sensitive samples. Contribute to method development for imaging liquids and solid–liquid