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The Machine Learning for Integrative Genomics team (https://research.pasteur.fr/en/team/machine-learning-for-integrative- genomics/) at Institut Pasteur, headed by Laura Cantini, works at
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, or supervised/unsupervised learning depending on the available data) using spatial analysis and geographic machine learning tools (e.g., scikit-learn, PyTorch/TF + GeoPandas/Shapely) - Implementing a semantic
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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
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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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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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. 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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/F) will work within the “RNA Architecture and Reactivity” unit and join the “Structure, Dynamics, and Targeting of Biomolecular Machines” team. This team currently consists of 6 researchers, 3
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archaeological and historical contexts is also required. Additionally, the ability to perform *ad hoc* data processing (multivariate statistics, machine learning, etc.) is desirable. Proficiency in programming
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generative artificial intelligence, machine learning, and ontologies to automatically align heterogeneous competency frameworks. The research will focus on: Formal modelling of competencies and educational