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Field
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machine learning is emerging as a promising avenue for solving problems in physics and engineering. Particularly, physics-informed neural networks (PINNs) have shown remarkable potential to solve problems
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The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The Faculty of Humanities, Education and Social Sciences (FHSE) at the University of Luxembourg brings together expertise from the humanities, linguistics,...
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Summary Research in the Ichinose Lab is in the field of vision and neuroscience. They use the mouse retina as a model system to investigate neural networks and physiological functions and have the
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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computational modelling using artificial neural networks. It brings together teams led by Mohamady El-Gaby (Oxford Experimental Psychology), Matthew Nour (Oxford Psychiatry), Rick Adams (UCL), and Maria Eckstein
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CBS - Postdoctoral Position: Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient
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. Research Content: 1. Study the brain network mechanisms of deep brain stimulation neuromodulation. 2. Research on neural biomarkers and closed-loop neuromodulation strategies and methods. 3. Clinical
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
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Differential Equations, and Graph Neural Networks. The objective is to measure and predict evolutionary forces and spatial cell interactions in healthy versus cancerous tissues, ultimately identifying