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methods for causal inference using large-scale observational healthcare data. The project will address fundamental methodological challenges in estimating causal treatment effects from longitudinal
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neuroimaging methods to better detect disrupted function following neonatal brain injury, and identify new more energy efficient learning algorithms that could reduce the economic and environmental cost of AI
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by Industry 5.0 technologies and human–robot collaboration. Key innovations include Digital Product Passports (DPPs) and data-driven tools to improve lifecycle transparency, alongside methods
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programme in the department and have the opportunity to join masters level courses (including courses on research methods). Moreover, our PhD students are supported to attend high-quality international
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characterisation techniques or laser systems Background in semiconductor device fabrication or cleanroom processing Experience with numerical simulations (e.g., FDTD, COMSOL, Lumerical) Programming skills (Python
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results and develop relationships using statistical methods and other analytical approaches such as finite element, and predictive techniques. Application Procedure • A 1-page cover letter, stating