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can be leveraged to accelerate learning from both classical and quantum data. The project will develop rigorous theoretical frameworks to understand key properties of quantum machine learning models
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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, perform and supervise research projects and collect data in agreed areas to a high level of competency and quality Take part in collaborative research projects within and outside the group Project manage
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if required Contribute to writing bids for research funding Analyse and interpret data Apply knowledge in a way which develops new intellectual understanding Disseminate research findings
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to help develop new techniques within the laboratory, help supervise project/undergraduate students, and present their data regularly at internal lab meetings and external conferences. All applicants should
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skills. They will be expected to help develop new techniques within the laboratory, help supervise project/undergraduate students, and present their data regularly at internal lab meetings and external
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, with opportunities to build an international research portfolio. The post holder will work on developing and testing study design and data analysis methods, particularly related to cluster randomised
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responsible for the entire project from polymer synthesis, crystallisation-driven self-assembly, characterising data collection interpretation. Training will be provided in all of the component techniques but
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themselves in the HPB medical oncology team to help deliver clinical care and trials and also integrate themselves into a research group at the university where they can generate preliminary data. Within
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supervision Conduct qualitative data collection, including semi-structured interviews and/or focus groups Analyse and interpret qualitative data using appropriate methods (e.g. thematic analysis) Use