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Field
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Modelling post combustion amine CO2 capture plant School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Prof Mohamed Pourkashanian, Prof Lin Ma, Dr Kevin Hughes
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, osteosarcoma) cell and time of origin is debated but experimental studies in different models suggest possible in utero induction with postnatal initiation similar to leukaemia. In Ewing sarcoma, FET::ETS gene
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(complexity). Develop and optimise a modelling pipeline including a decision support dashboard for optimal patient selection for surgery to ensure daily surgery caseload optimisation, post-operative care
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creativity and cross-discipline collaborative ability as appropriate. C3 Excellent communication skills (oral and written), including public presentations and ability to communicate complex data/concepts
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resources efficiently, and clearly convey complex information. Previous experience with decarbonization in the transport sector, LCA, EE-MRIO models, sampling design, and emerging cities including working
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to ensure AI models deliver reliable, transparent, and auditable decisions in complex industrial contexts. This project offers an exciting opportunity for you to shape the next generation of industrial AI
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shift in the world of hardware design. On the one hand, the increasing complexity of deep-learning models demands computers faster and more powerful than ever before. On the other hand, the numerical
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is characterised by complex and highly dynamic turbulent flows that define the performance and design of renewable energy systems and their infrastructure. This PhD project aims to enhance
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can be varied. Crucially, the models we derive will be validated by real-world measurements to ensure our simulation environments are realistic and scalable to more complex radar networks. This will
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity