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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—expressivity
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statistical models, with the support of project supervisors. Support writing of research outputs for academic and lay audiences, and contribute to the development and running of stakeholder engagement
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students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration
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for publication, research seminars etc Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and
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guidance to PhD students where appropriate to the discipline Contribute to developing new models, techniques and methods Undertake management/administration arising from research Contribute to Departmental
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challenge due to limited patient data—especially at the single-cell level—making traditional modelling approaches difficult. This project tackles that challenge by integrating multi-omics and clinical data
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pioneering, cross-disciplinary research that integrates AI, energy systems and advanced mathematics. This position aims to advance the next generation of battery modelling and control strategies by combining
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of High throughput Calculations Fluency in relevant models, techniques or methods and ability to contribute to developing new ones High level analytical capability Ability to communicate complex information
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artificial intelligence methodologies. The successful candidate will work at the forefront of computational biology, developing novel approaches for large-scale genomic data analysis and contributing
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Role Description A research position is available to support research to enhance observations of human exposure to air pollution and other pollutants using sensor-based approaches. Air pollution is