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Field
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Although the development of systems architectures and models has been an established practice for many years, the assessment of the quality of models has relied on expert judgement with
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how standard production practices impact biodegradability is essential for creating more sustainable textile alternatives. This research aims to develop a robust framework for measuring biodegradability
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. Building upon existing expertise in porous media modelling within the Thermo-Fluids Research Group (UoM), the project will analyse and volume-average these data to develop targeted refinements or propose
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input needs, accompanied by a boost in algorithmic development, e.g., multi-modal learning, transfer learning, federate learning, and knowledge embedding, etc. However, a significant motivation of
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identifying and prioritising conservation action especially challenging. This project aims to develop a low-cost, field-based, portable and easy-to-use Lab-on-a-Chip device for environmental DNA monitoring
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control laws into Trent gas turbine engines and developed algorithms monitoring fleets of 100s of engines flying all around the world. During the PhD, you will have the opportunity to deeply engage with
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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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project offers a unique opportunity to develop a cutting-edge genomic epidemiology toolkit for real-time fungal surveillance. You’ll optimize DNA extraction protocols using advanced enzyme-based methods
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of measles evolution and naming systems (nomenclature) to support public health identification of measles outbreaks/importation and ultimately, elimination. In this project you will learn to perform genomic
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the ranking. However, STV method becomes considerably more complex with encrypted ballots. Our goal is to develop an algorithm/protocol to count encrypted ballot using the STV method. Our first point of