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: 10.1101/2025.09.08.674950), and AI/machine learning. We work closely with clinicians to translate our findings into clinical practice, focusing on genomically complex sarcomas and haematological
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical techn ology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess
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health, epidemiology, statistics, biostatistics, or machine learning/artificial intelligence. You must have a strong academic background from your previous studies and have an average grade from your
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About the role: Applications are invited for a 4-year postdoctoral research position to work collaboratively between the teams of Alexis Barr (MRC LMS) and Maria Secrier (Dept. of Genetics, UCL). We
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. Recent advances in digital pathology and innovative data analytics including machine learning have enhanced our ability to identify clinically relevant spatial characteristics of TMEs [1]. In lung cancer
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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