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learning. Experience with deep learning algorithms for object segmentation/recognition is also welcome. Proficiency in C++ and Python. Proven track record of high-impact publications in related fields
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to peers and to a larger audience – Willingness to learn, adapt, and be receptive to feedback – You are proficient in written and spoken English – You have experience with characterization
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Brain Barrier (BBB), CNS Drug Delivery, Brain Shuttles, Brain Imaging, Medicinal Chemistry, Computational Science, Artificial Intelligence (AI), Machine Learning. MAIN SUB RESEARCH FIELD OR DISCIPLINES1
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programming, CAD or generative design tools, knowledge in crystal plasticity, continuum mechanics, additive manufacturing, data science, and machine learning. Additional comments More information about the
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(willing to learn and conduct experiment on animals), engineering, pharmacy, biochemistry or related. Engineering background is of particular interest. - Strong experience in in vivo electrophysiology and
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knowledge of the rules governing microbial assemblages in order to propose practical solutions for improving wetland management and governance. The specific objectives of the MAEWA project are to acquire new
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experiments and numerical simulations and will be divided into three parts: Microstructure: 1.1. Experimental Characterization: Using X-ray tomography, image analysis with conventional tools or deep learning
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in developing and applying niche models, and population models. Experience with machine-learning will be considered as a plus. Knowledge of paleogenomics, population genetics and isotope geochemistry
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ejection (CME) impacts, but also outside CME periods, when plasma jets are detected. It will involve developing a machine-learning detection tool to extend the event databases corresponding to conjunctions
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be willing to learn French (a language course will be paid for by the ILL) Further information can be obtained by contacting Stéphane ROLS, Head of the Scientific Computing Group: +33 (0)4.76.20.78.25