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drive the gradual development of these technologies toward real-world applications. This involves engineering experimental hardware for cell culturing workflows, optimizing experimental processes, and
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optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
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in a more accurate analysis of optimizing the service performance. Computer vision approaches such as ones for object identification and action recognition can help to automatically identify deviations
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optimal operating conditions and followed by surface analysis techniques (e.g. Scanning electron microscope, X-ray diffraction for residual stress measurements, Electron Back-Scattered Diffraction and
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scalable surface engineering methods and state-of-the-art permeation analysis techniques, the project will optimize coatings for alloys such as steel, aluminium, titanium, and nickel. The project will use a
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image velocimetry approaches. This enhanced understanding is crucial for optimizing performance, and educate the design of future architectures. Additionally, the research accelerates the design and
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to developing novel computational methods for design and optimization problems in turbomachinery with strong support from Rolls Royce plc. The student will be expected to closely work with Rolls Royce Engineer
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significance as it seeks to provide a deeper understanding of CRUD formation and its influence on heat transfer dynamics. The investigation aims to contribute valuable insights to enhance the safety and optimal
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of dehydration using a low-power radio-frequency (RF) sensor. The research objectives include design optimization to improve wearability, robust data acquisition using machine learning and establishing correlation