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mental health omics data more harmonized, accessible, understandable, and useful to researchers, clinicians, industry, and people with lived experience. You will work closely with a multidisciplinary team
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an individual Resistant, Tolerant, or Susceptible (R/T/S) to a virus? That is the key question we aim to answer in this PhD study. We have gathered data on viral outbreaks over ~20 years and sequenced hundreds
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, are crucial for modelling volcanic processes and are vital for understanding the transitions. Geophysical monitoring provides essential information to constrain these parameters and inform decision
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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their application materials which of the three projects they are most interested in working on. About you You are working toward a doctorate in a social science discipline (or a relevant data science field), have
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their application materials which of the three projects they are most interested in working on. About you You are working toward a doctorate in a social science discipline (or a relevant data science field), have
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applications, data collection, and dissemination. The role will involve qualitative research such as discourse analysis, interviews, participatory workshops with carers and stakeholders, and data coding
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in the study of problems in quantum many-body physics through the lens of quantum information and quantum computing? Do you enjoy studying complex quantum mechanical systems both from a physical and a
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they are effective in this population of CYP and their families. The project is likely to include conducting systematic reviews, secondary data analysis of social care data and obtaining access and analysing data held
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap