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computational lung modelling. Working with our proprietary respiratory system model, you will generate large-scale, high-quality virtual airflow and acoustic datasets. You will then develop and train state
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data processing pipeline capable of handling complex behavioural datasets from biologgers and autonomous monitoring units, enabling accurate movement and interaction analysis without reliance on GPS
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an efficient and mature technology, yet it requires high temperatures and has a large carbon footprint. This PhD project addresses a key challenge: efficiently producing bio-methanol from abundant
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sponsors own security checks before starting the PhD. Start date: 5 October 2026 Closing date: 27 February 2026 For further information please email Professor Chris Gerada (University of Nottingham) and
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acoustic data, has shown transformative potential in domains such as healthcare, environmental monitoring, and autonomous systems. However, most advances rely on large datasets and computationally intensive
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University. You will conduct applied impactful research around the monitoring of the training process, and support practical data collection within sport science support at CPFC aligned to this research
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The production of primary steel and aluminium uses a huge amount of resources and causes significant environmental damage, leading to a large effort to recycle these high-value alloys. However, the
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convert solar energy into pressure potential energy, akin to pumping water up to an elevated reservoir but without the need for large natural elevation changes. A great idea in principle, but one which
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At the University of Vienna, over 10,000 people work together on the big questions of the future. Approximately 7,500 of them are academic staff members. These are individuals who, with
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catchment hydraulic models and sewage network models, leveraging AI and other "big data" meta-analysis techniques to perform this in a computationally efficient manner. Provide guidance as to which sites and