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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 2 months ago
into resources that can be used for machine learning. The PhD will therefore investigate multimodal approaches that connect visual sign-language information with textual representations under low-resource
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modern machine learning, with broad implications for both theory and applications. The PhD project focuses on the theoretical foundations and algorithmic design of discrete gener- ative models, situated
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comparison with predictions from machine learning models • Close collaboration with researchers in charge of machine learning, algorithmic architecture and performance analysis • Contribution to the scientific
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Eligibility criteria The recruited person must have expertise in cosmology, numerical development and machine learning. They must be proficient in the Python programming language, with experience in JAX being a
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of Research Experience1 - 4 Additional Information Eligibility criteria We are looking for a doctor in particle physics with less than two years of experience after the PhD. Experience in machine learning and
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data) will help validate observations and refine predictive models. Automated monitoring tools (scripts, dashboards, alerts) incorporating machine learning algorithms or statistical methods will be
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. Unsupervised machine learning approaches will be used to identifiy key dimensions of circadian rhythm associated with dementia subtypes. This requires a very good level in statistics and R programing as
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(https://endomic.github.io) aims to develop artificial intelligence and statistical-learning methods capable of identifying robust and clinically meaningful disease endotypes from heterogeneous, multimodal
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ElastoGravity Signals. Journal of Geophysical Research: Machine Learning and Computation, 1, e2024JH000360. https://doi.org/10.1029/2024JH000360 Juhel, K., Hourcade, C., & Bletery, Q. (2024). PEGSGraph : GNN
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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