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| £20780 + £2500 industry top up (per annum (tax free)) Overview This exciting, fully-funded PhD opportunity invites applications from candidates with a robust foundation in data science, modelling, and
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invites applications from candidates with a robust foundation in data science, modelling, and/or engineering, and a keen interest in deploying data analysis and artificial intelligence (AI) to solve real
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of the most common malignancies in women. Particularly, triple negative breast cancer (TNBC) has a high rate of recurrence and is more challenging to treat due to the absence of oestrogen receptor, progesterone
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natural language processing, Dr Matthew Hitchings, who will offer cutting-edge insight into high-throughput sequencing and a strong background in comparative genomics and bioinformatics and Professor Lewis
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. The system will leverage cutting-edge techniques in Natural Language Processing (NLP), Machine Learning (ML), and Multimodal Analysis to conduct adaptive interviews, assess candidate responses, and generate
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will generate a wide range of contemporary high-resolution next-generation sequencing and array-based genomic and epigenomic datasets across large cohorts of human tumours and experimental models
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process conditions. Furthermore, this research will focus on the development of a model, allowing for virtual testing and optimisation of the chemical recycling process. This includes potential
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dynamics and tissue morphogenesis during embryo development using cellular, molecular and mechanical approaches. Cell movements underlie tissue patterns and shapes. Using chick embryos as the model system
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An exciting opportunity has arisen for a talented computer scientist to join our team as a researcher within the Green Algorithms Initiative in the Department of Public Health and Primary Care, one
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process, and this process itself can impede certain policy. This project involves summarising models of political choice (e.g. the median voter, probabilistic voting, citizen candidate, etc models) with a