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03.06.2021, Academic staff The Albarqouni lab develops innovative deep Federated Learning (FL) algorithms that can distill and share the knowledge among AI agents in a robust and privacy-preserved
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware
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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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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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heating and cooling, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: Advanced deep learning architectures Mathematical foundations of machine
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. Hu, X. Wei, X. Wu, J. Sun, J. Chen, Y. Huang, J. Chen, A deep learning-enhanced framework for multiphysics joint inversion, Geophysics, 88(1), K13-K26, 2023. https://doi.org/10.1190/geo2021-0589.1 [3
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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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The rapid growth of deep learning has come at an extraordinary environmental and computational cost, yet the standard training paradigm remains remarkably unchanged. Every sample is passed through