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and evaluation of the developed methods using relevant imaging datasets and downstream computer vision tasks. This appointment is subject to UCL Terms and Conditions of Service for Research and
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in Computational Electromagnetics will be responsible for developing theoretical models and high-performance computer codes for modelling the interaction between electromagnetic waves and dielectric
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limited to machine learning, generative and foundation models for biology, digital twins, mechanistic and hybrid modelling, statistical inference, or AI-enabled experimental design. We are particularly
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PhD in a relevant field (e.g., Computer Science, Physics, Mathematics, Economics) and demonstrate expertise in financial computing, quantitative finance, machine learning, complex systems modelling
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capacity for acquiring skills and knowledge in theoretical biology broadly, including evolutionary modelling practices, comparative methods, and abstract conceptual reasoning Willingness to acquire new
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progression and shape the response to anti-cancer therapy. Recent advances in digital pathology and innovative data analytics including machine learning have enhanced our ability to identify clinically relevant
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compounds in Human Derived Biliary Tract Cancer preclinical models. Supervisors: Chiara Braconi and Sergi Marco Project summary: Biliary Tract Cancers (BTC) are tumours arising from the bile duct within and
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transcript and protein levels. Using machine learning, we will identify conserved expression profiles that predict lifespan outcomes. Guided by these insights, we will use state-of-the-art genome editing in