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, clinical measurements, medical imaging and longitudinal health data provides opportunities for Artificial Intelligence (AI) and Machine Learning (ML) to improve cardiovascular risk assessment and prediction
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is focused on using spatial profiling and machine learning of human specimens in combination with functional experiments in animal models to understand cancer initiation, progression, and metastasis
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data-quality agents, predictive models that can transfer across machine families, prescriptive decision support, operational dashboards, and ensuring the delivery of methodologies, documentation
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Requirements Bachelor’s degree (minimum) in Electrical / Electronics Engineering, Computer Engineering, Computer Science, Robotics, or a closely related discipline, with foundational knowledge in signal
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with strong foundations in modern machine learning and the ambition to advance foundation models, agentic systems, and new AI approaches for high-impact scientific applications. We seek candidates with
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Fellow to develop and evaluate artificial intelligence methods for physical medical procedures. The fellow will design and implement machine learning models to analyze procedural data, support clinical
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Digitally enabled development of sustainable metamaterials to create circular alternatives to current unsustainable parts widely used in industry School of Electrical and Electronic Engineering PhD
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young team (Computational Systems Medicine) within the context of the BMFTR-funded DIASyM (https
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Primary supervisor Levin Kuhlmann Research area Machine Learning This project is similar in flavour to the Conscious AI project but rather than come from a Philosophical/Neuroscience/Math/Theory
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programming background with fundamentals in machine learning and data science. Experience in building visualisations and interactive immersive environments (using game engines like Unity3D) is