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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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costs and energy requirements of state-of-the-art deep learning models significantly, while democratizing them for a vast community of users, researchers, and practitioners. The task is to perform just
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. Lemaire, B. Miramond, et al. An analytical estimation of spiking neural network energy consumption. IEEE Transactions on Neural Networks and Learning Systems, 2022. C. Lu, H. Du, W. Wei, et al. Estsformer
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elucidating the molecular and cellular mechanisms of the late phase of long-term potentiation (LTP), a key process in learning and memory. The project is based on the development and use of an innovative
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automated configuration mechanisms based on fingerprinting and machine learning to ensure traffic analysis remains faithful to the behavior of the monitored machines. Finally, you will validate your solutions
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-funded LEARN project uses high-quality, cross-national data and advanced analytical techniques to investigate the key processes through which major disruptive events affect children’s educational
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, materials science, and physics. Supported by 19 countries, the ESRF is an equal opportunity employer and encourages diversity. Context & Job description Thesis subject: Machine Learning for Neutron
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and erosion for 60 years. One of the main objectives is to acquire fundamental knowledge about the processes controlling environmental risks related to the dynamics of metal contaminants (speciation
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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on the ERC-funded project ‘Understanding the Consequences of Major Health Crises for Education: Learning from the COVID-19 Pandemic (LEARN)’. The LEARN project: Health crises, natural disasters, and violent