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-informed machine learning (PIML) with domain-specific engineering knowledge. By embedding physical laws and corrosion mechanisms into data-driven models, the research will produce more accurate
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personalised, ethnically-stratified risk scores. This is a highly interdisciplinary project at the intersection of machine learning, health equity, and precision medicine. The successful candidate will join a
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are open to adjacent areas that can interface with SERENE’s mission): AI/ML & Data-Centric Space Environment Modelling Machine learning, deep learning, Bayesian methods, or hybrid physics-ML models
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underpin our research and provide an exceptional learning environment. EESE delivers internationally recognised research and education across communications and sensing, electrical power systems, cyber
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may also explore embedding these new computational methods into optimisation and machine learning contexts. The new computational techniques developed will be geared towards the following key
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, flight dynamics and operations. Stability and control of air vehicles using novel methods and/or for innovative platforms. Application of novel machine learning approaches to practical future flight
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of Birmingham is inviting applications for a Research Fellow position focused on Machine Learning for Automated Formal Verification. Machine learning has transformed programming, with code generation rapidly
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postdoctoral Research Fellow (RF) position for one year with a possible extension for one more year. The starting date is November or December 2025. This post will advance the application of Machine Learning (ML
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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testing of machine learning/AI algorithms Integration of radiomic and biological datasets Working closely with Medical Physics colleagues on reviewing recommendations for detection of specific metabolites