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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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Historic Avant-Garde (Auto)biographical/Testimonial Literature in Latin America Photographic Narrative in Mexico Brazilian History Culture and Society Political Cinema and Documentary Sociolinguistics of the
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protein expression across tissues and developmental stages, quantifying correlations between transcript and protein levels. Using machine learning, we will identify conserved expression profiles
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University Edinburgh University ST Andrews University UCL Durham Fees and funding Fees and funding Fees 2026/27 UK: £5,238 International & EU: £27,720 Fees are based on the annual fee for full-time study
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years part-time; Thesis of 30,000-40,000 words Apply now Overview Overview Our research strengths include: Scottish military history Medieval warfare Early modern warfare The Napoleonic Wars Colonial
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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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to recognise the shape, size, and materials of urban features, through the application of statistical, machine learning (ML) and artificial intelligence (AI) techniques. The use of freely accessible raw GNSS
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expression across tissues and developmental stages, quantifying correlations between transcript and protein levels. Using machine learning, we will identify conserved expression profiles that predict lifespan
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the development of valuable transferable and personal development skills, such as finance-oriented computer programming, presentation and professional writing skills. As a PhD student in Accounting