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Field
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background and expertise in one or more of the following areas: High-dimensional probability and concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural
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statistical mechanics, probabilistic approaches to quantum field theory, stochastic partial differential equations, rigorous approaches to the renormalization group, and related areas will receive special
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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(Garratt et al. 2015), thereby limiting the temporal variation in adult demographic parameters. In his monograph on stochastic demography, Tuljapurkar (1989) proposed a theoretical way to quantify
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, or a related field. Candidates must possess relevant research experience in probability, stochastic analysis, and optimization. Applicants with knowledge in machine learning, as well as a track record
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
and theoretical questions related to statistical modeling, prior design in the Bayesian framework, convex and non convex optimization, stochastic optimization. He/she is expected to develop commented
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models. Experience and knowledge of stochastic differential equations. Experience of using MHD output as input for GCR transport models Theoretical knowledge of particle transport in plasmas Documented
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», https://probabilistic-qft.org/ , which addresses broad theoretical questions interfacing quantum field theory and probability theory, but also noisy quantum dynamics. We welcome applications of candidates
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | about 1 month ago
Generative Modeling through Stochastic Differential Equations.” ICLR. [4] Pidstrigach, Jakiw. 2022. “Score-Based Generative Models Detect Manifolds.” NeurIPS. [5] Dupuis, Benjamin, Dario Shariatian, Maxime
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of Mathematical Statistics include stochastic models, statistical theory and computational statistics, probability theory and statistical signal processing, with applications in areas such as financial mathematics