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Inria, the French national research institute for the digital sciences | Saint Martin, Midi Pyrenees | France | 2 months ago
-parametric Surrogates with Active Learning, SC AI4S 2024: https://hal.science/hal-04712480v1 Training Deep Surrogate Models with Large Scale Online Learning, ICML 2023: https://hal.science/hal-04102400v1 Loss
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tracking and autoencoders for amplitude and phase noise characterization Bayesian filtering Building experimental set-ups for noise characterization Reinforcement learning strategies for comb generation
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communication and collaboration skills Preferred: Experience with simulation-based inference and Bayesian methods Familiarity with cosmological simulations or observational cosmology ML architecture design and
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measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more
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focus on the following areas: Subspace tracking and autoencoders for amplitude and phase noise characterization Bayesian filtering Building experimental set-ups for noise characterization Reinforcement
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transportation systems modeling and simulation that could involve integrated machine learning and network equilibrium/simulation, surrogate models/ reduced order emulators or Bayesian or interpretable machine
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designs such as observational study, randomized clinical trial, adaptive randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis Work
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randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis. · Develop as an educator by taking an active teaching role in POCUS and EBM
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general working in the Higham Laboratory, led by Professor Tom Higham (https://highamlab.univie.ac.at/ ). DISPERSE (ERC Advanced grant: Higham) The “Disperse” ERC project will explore the key steps in
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insurance coverage Employee discounts programs For detailed information on benefits and eligibility, please visit: http://uhr.rutgers.edu/benefits/benefits-overview . Posting Summary The Department