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the "Machine Learning and Gene Regulation" team led by William Ritchie, specializing in bioinformatics and post-transcriptional regulation. The scientific environment at the IGH — international seminars, journal
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research environment involving several PhD students and postdocs, and strong regional and international connections, with collaborations with the University of Geneva and CERN. Terms of Employment
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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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inspiring novel strategies for brain-machine interfaces. Indeed, some studies have found HD signals to be anticipatory, depending on the degree to which movements are active, volitional or predictable. After
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, soil, and plants aid in the collection of real-time data directly from the ground. Based on these historical data predictive machine learning (ML) algorithms that can alert even before a problem occurs
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of EOM 3D anatomy and ex vivo contractility data, high-speed video oculography and head tracking in mice using deep learning-based pose estimation, and implementation of Hill-type muscle models
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-phase heterogeneous photocatalysis would be a plus - A first experience in analytics related to photocatalytic gas-phase reactions would also be a plus - Fluency in French or English, willingness to learn
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the domain of molecular simulation of physico-chemical processes in proteins. The PhD student will have access to the computer cluster of the lab and to national supercomputers of the GENCI. [1] R
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that they can integrate it into their large-scale quantum computer system engineering models. SKILLS. Candidates must have a high-quality background in quantum information or quantum physics, and an
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, or ability to learn quickly) and/or symbolic/numerical computation (Mathematica, R, Python). • Fluency in scientific English (reading, writing, speaking) is essential to interact with the project's