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machine learning. One arm of the project will seek to engineer diverse quantitative features (e.g., adapting concepts and metrics from network science [5] to characterise cellular graphs) of the spatial
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refinement of an implementation toolkit, aimed at supporting local adoption and enabling future national rollout. Working closely with the Chief Investigator, Prof. James Kirkbride (UCL), and Deputy Chief
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interventions to extend human healthspan. Techniques: Data mining Transcriptomics Proteomics Statistical Analysis Machine Learning Genetics Molecular Biology References: Developmental mitochondrial complex I
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to understand complex problems including complex statistical analysis, data linkage, longitudinal epidemiological and advanced meta-analysis, but also in depth qualitative techniques and the analysis
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extend human healthspan. Techniques: Data mining Transcriptomics Proteomics Statistical Analysis Machine Learning Genetics Molecular Biology References: Developmental mitochondrial complex I activity
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to query and evaluate topical questions in the parallel/convergent basis of complex phenotypes. This project will build on existing datasets and with opportunity for new experiments. The ideal candidate will
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mental health and health behaviours over the lifecourse discovering mechanisms which can modify these processes and have the potential to improve public health in a complex and changing world developing
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information of increasing complexity pose significant challenges to our individual and collective use and curation of information. Fundamental concepts such as identity, memory, authenticity, trust
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in fertility and increased incidences of male and female reproductive dysfunction. Although the etiologies of these conditions are complex, there is increasing evidence that they are associated with
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, and how these molecules interact within cells to form complex functional networks. We are also working towards applications of our knowledge to address important real-world problems. PhD: 3-4 years full