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position is part of a large-scale research project around immune response in neurodegenerative disease. The PhD student will be part of a data science team working on data analysis and experimental design as
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mismatch remains a key issue for speech and language technologies. Especially for speech technology the variability of input data is large and recordings can occur in highly complex acoustic and linguistic
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-on experiments & development and opportunities for travel to international facilities. This project is supported by the large investment of a URKI Future Leaders Fellowship to identify, synthesise and explore new
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for example synchrotron based experiments. More information on these facilities can be found; There will be many rewards: Working as part of a large and experienced team across the cohorts will ensure a
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V236E (ε3), which reduce the risk of AD by 2–3 times. This project will use bioinformatics and big data and induced pluripotent stem cell (iPSC) models —cells generated in the lab that can mimic brain
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health datasets. Ageing is usually quantified as a measurement of the time elapsed since birth. This cannot explain the large variations in ageing trajectories between older people of similar age
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to test data, which are therefore only useable within a narrow range of scenarios. These limitations result in the requirement of large number of high-cost experiments being conducted to populate the models
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local extinction of large-bodied frugivorous birds and mammals, leaving structurally intact yet defaunated forests missing key ecological functions like seed dispersal for large-seeded plants. Studying
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, manipulate large datasets, visualise data and perform numerical and statistical analysis is a requirement. Experience in handling 'big data', machine learning and working in distributed teams, is useful
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quantitative and qualitative data. This project is likely to involve undertaking a systematic review; interrogating and analysing large-scale healthcare datasets; and using qualitative methods, such as