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-based, and machine-learning approaches - Identifying markers of intelligent, intentional, and malicious manipulations of detection systems - Developing detection and classification methods to distinguish
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, or computer science. Core competencies: solid background in quantum many-body physics strong programming skills (Python required, Rust a plus) experience with tensor networks, variational Monte-Carlo, machine learning
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develop a new generation of hybrid models combining large-scale machine learning with physical knowledge to represent interactions between mobile robots and their environment. The research will address
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to training and supporting PhD students, postdoctoral researchers, engineers and clinicians in the use of these pipelines. The position is based at the Grenoble Institute of Neurosciences, in close interaction
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different dyadic motor coordination tasks. A range of neurophysiological measures (EEG, ECG and fNIRS) as well as behavioural measures will be recorded simultaneously from both partners. Machine-learning
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language processing that address concrete problems and are both theoretically rigorous and interpretable. The PhD is funded by the ERC CoG PANDORA (Deep Multimodal Learning for Mining and Generation of Arguments
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Eligibility criteria Selection will be based on the following scientific and technical criteria: • PhD in computational biology, machine learning, bioinformatics or a related field. • Proficiency with Python
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following competencies: applied mathematics, statistics and probabilities data science, machine learning, artificial intelligence optimisation power system management, integration of renewables energy
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multidisciplinary unit, with expertise in mathematical modelling and machine learning, wet-lab expertise in multiplex serological and genetic assays, expertise in diagnostic development and production, and expertise
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., Reinforcement Learning in Different Phases of Quantum Control, Phys. Rev. X 8, 031086 (2018). [8] J. Biamonte et al., Quantum Machine Learning, Nature 549, 195 (2017). [9] E. Célanie, L. Delisle, and A. Jaouadi