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Field
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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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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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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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electronic health records (EHRs) from multiple UK hospital centres using advanced data analytics including machine learning, deep learning, and statistical techniques—with a particular emphasis on deep
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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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, focusing on high-performance deep learning for neural implicit reconstruction of ultrasound data. The goal is to advance the scalability and efficiency of neural radiance fields (NeRFs) and related
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will disrupt today’s most vibrant research frontiers: Embed model-based AI into self-supervised pre-training pipelines Finetune multimodal deep learning models that answer diagnostic questions about X
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be the state estimation of the robotic system from external cameras. Familiarity with existing methods from these domains, such as Deep Learning, Quality-Diversity algorithms, reinforcement learning
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expertise in analysing/ training models on biological or chemical datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep