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testing) to understand and tailor the physical and chemical interactions within these complex structures. Cranfield University is internationally renowned for its research into materials for extreme
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to symmetry breaks in the patterning process. A hybrid modelling approach integrating the dynamics of a core network while utilising a virtual template from experiments for cellular growth and division will be
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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global warming, but it remains unclear whether it would be effective in practice. CCM relies on the idea that emitting small amounts of aerosols at high altitude could modify the optical or physical
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with mechanical testing of the alloys in hydrogen rich environments will be used. The ReNU+ CDT has hosted a number of online pre-application workshops to provide guidance on the application process
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class degree in an Physics, Mathematics, Information or Electrical Engineering or related subject. Applications should be submitted via the University of Cambridge Applicant Portal https
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
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online pre-application workshops to provide guidance on the application process. Watch recordings. Start Date October 2025 or January 2026 Award Duration 4 years Application Closing Date 12th September
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to symmetry breaks in the patterning process. A hybrid modelling approach integrating the dynamics of a core network while utilising a virtual template from experiments for cellular growth and division will be