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, machine learning and AI approaches. Empower biologists to understand their datasets, using our broad training portfolio to enable data curiosity and develop analytical skills. Design innovative approaches
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duties and responsibilities Carry out research in modelling and simulation of particulate processes using appropriate software packages for techniques such as DEM, CFD, FEA and Machine Learning. Develop
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, data integration, and machine learning methods across large scale multi-omics datasets. The Barr and Secrier teams have successfully worked together over the last five years, leading to three joint
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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 spatial
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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
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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