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with ab initio calculations or mineral equations of state; familiarity with MAGEMin, BurnMan; knowledge of Bayesian methods (MCMC) or neural networks applied to geophysical problems; publications in
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
following areas: High-dimensional probability and concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations
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-fidelity optimization, neural architecture search, or large-scale AutoML systems. Familiarity with surrogate modeling, physics-informed neural networks, or uncertainty quantification for scientific
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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking
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. Experience applying machine learning to networking problems, for example, reinforcement learning, graph neural networks, or uncertainty quantification. A track record of publications in leading networking
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Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig | Leipzig, Sachsen | Germany | 2 months ago
and Alzheimer’s disease Key research methods: fMRI (including 7T), MEG, virtual reality (VR), a wide variety of cognitive tasks, psychophysics, machine learning, deep neural networks, and computational
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machine learning, with demonstrated experience in developing and training neural networks for predictive modeling. Position Requirements: The successful candidate is expected to: Build and evaluate chemical
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network methods. The posts will focus on classical benchmarking, including Pauli propagation, tensor networks and related numerical methods for simulating smaller systems and comparing them with quantum
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
. Leglaive, L. Girin, X. Alameda-Pineda, and R. Séguier, "A multimodal dynamical variational autoencoder for audiovisual speech representation learning," Neural Networks, 2024. 10. A. Ballou, X. Alameda-Pineda
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mentorship agreements with each team member. The INP is a center of training and research that combines basic and translational research to study the organization, function and interaction of neural cells, as