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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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-critical decisions in real time. These systems rely heavily on sensor data (e.g., GPS, pressure transducers, image processors), making them vulnerable to stealthy threats like False Data Injection (FDI) and
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At Aston Institute for Membrane Excellence , we are passionate about equity, diversity, and inclusion and are dedicated to cultivating a diverse cohort of PhD researchers from a range of academic
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changes (so called swelling). Swollen batteries are at risk of rupturing which may significantly shorten their lifetime. Development of advanced computer models is critical for understanding and
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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into car cabins (to reduce the exposure of occupants) and the regions where pollutants accumulate (to be avoided by pedestrians and cyclists) need to be minimised. The PhD objective is to study the effects
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This fully-funded PhD research opportunity, supported by EPRSC Doctoral Landscape Awards (DLA) and Cranfield University offers a bursary of £22,000 per annum, covering full tuition fees. This PhD
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models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based
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bottleneck in the screening process. This PhD project will address this through deep integration of scanning probe electrochemistry, optical microscopy and machine vision, to develop a system that can
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teach at the University of Vienna, where more than 7,500 academics thrive on curiosity in continuous exploration and help us better understand our world. Does this sound like you? Then join our