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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph
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underlying sleep, a fundamental and evolutionary conserved behavior. We are studying the homeostatic and circadian mechanisms regulating sleep, and also have deep interest in understanding the functions
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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, Mathematics, Physics, or a closely related field. Proficiency in machine learning libraries (e.g, scikit-learn, PyTorch, and transformers) and data analysis tools (e.g., pandas, NumPy, and CuPy). Hands
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rigorous, collaborative research aligned with project goals. Develop and apply deep learning models, particularly in computer vision, NLP, and multimodal systems. Publish in peer-reviewed journals and
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integration. Lead and contribute to research involving AI-powered and AI-enabled robotic systems, including deep reinforcement learning, computer vision, and human-robot interaction. Facilitate strategic
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applications of deep learning, medical imaging, and biomarker integration. This full-time, one-year position offers a unique opportunity to engage in impactful research at the intersection of AI, connectomics
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deep learning frameworks (e.g., PyTorch, TensorFlow, and JAX). • Experience in PDE/ODE modeling and numerical methods. • Strong interest in interpretable ML and mechanistic model discovery. Submit a
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, Mathematics, Physics, or a closely related field. Proficiency in machine learning libraries (e.g, scikit-learn, PyTorch, and transformers) and data analysis tools (e.g., pandas, NumPy, and CuPy). Hands