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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
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; a global network of campuses and partners for students and faculty to leverage for learning and research; a deep investment in lifelong and experiential learning; a premium placed on pedagogical
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the project: Development of a regional normative BrainAGE model for morphometric measures. The position is part of the EU-funded Marie Skłodowska-Curie Actions Doctoral Network MenoBrain “Brain Health in
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, 2022) and extended this to the triple equivalence between neural dynamics, Bayesian inference, and algorithmic computation (Commun Phys, 2025). -We validated it within in vitro neural networks (Nature
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experience in AI teaching We understand AI very broadly – and adequate experience would include most topics in modern statistics and topics like Bayesian Machine Learning and Simulation Based Inference (a past
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following areas: Probabilistic or Bayesian Machine Learning Variational Inference, Ensemble, or Diffusion Models Spatio-Temporal or Sequential Modelling Graph Neural Networks Deep Learning and Uncertainty
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of the Biomaterials and Tissues of the Future. https://cordis.europa.eu/project/id/101226431 This network has 8 host institutions hiring doctoral candidates: Uppsala University, Universitat Politecnica de Catalunya
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. Desirable Expertise in computational fluid mechanics, broadly construed. Expertise in Bayesian methodology for optimization and experiment design. Experience with equivariant neural networks. Track record
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experience would include most topics in modern statistics and topics like Bayesian Machine Learning and Simulation Based Inference (a past research focus on neural network architectures is not a prerequisite
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constituting a large national and international network of potential collaborators. Where to apply Website https://emploi.cnrs.fr/Candidat/Offre/UMR7164-SANMER-033/Candidater.aspx Requirements Research