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biological questions, advancing deep learning models, or other topics discussed with the PI. We use publicly available and simulated genomic data. Core job duties include: (1) Building computational
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Computational Fluid Dynamics (CFD), fluid mechanics, and Artificial Intelligence (AI), with a particular focus on developing deep reinforcement learning methods for active flow control of hydraulic
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implementing, training, and evaluating machine-learning or deep-learning models. You are not expected to already be an expert in both areas, You are genuinely motivated to develop expertise in the
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integration into multi-model and multi-beamline measurement systems. Ths position requires a deep understanding of X-ray Absoprtion Spectroscopy and prior experience with methods of machine learning
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longitudinal, personalised fracture-risk trajectories using repeat imaging. The successful candidate will therefore be working primarily as an AI/image-processing researcher, developing deep-learning models
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Vision Specialized areas: Deep Learning, Generative AI, Prompt Engineering, Conversational AI and Chatbots, Reinforcement Learning Applied domains: Machine Learning for Cybersecurity, AI for 3D
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Computer Vision Specialized areas: Deep Learning, Generative AI, Prompt Engineering, Conversational AI and Chatbots, Reinforcement Learning Applied domains: Machine Learning for Cybersecurity, AI for 3D
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) Pathfinder project NOAH, you will design, train and implement ARCA: an AI foundation model for crop microbiomes. You will work at the interface of deep learning, bioinformatics and microbial ecology, using
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its usage. analytics About this tool (Opens in a new window) Skip to main content PhD Studentship: Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning
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advancements in machine learning, deep learning, AI and data-driven intelligence. The Research Software Engineer will collaborate with interdisciplinary teams on campus to develop innovative AI solutions that