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impact of the Isles of Scilly Community Research Network (CRN) , designed to support research that is locally led, co-designed, innovative and community-driven. This fully-funded PhD studentship offers a
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, management, quantitative analysis Lead in publication outputs in appropriate academic and industry fora The role-holder will also have management, liaison and networking responsibilities. The skills
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comprehensive analysis of the extensive Pulse dataset, uncovering latent patterns and taxonomies that define building leakage characteristics. Surrogate Model Development: You will develop data-driven surrogate
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latitudes. Student profile: The project requires the student to be relatively proficient in Python programming to use and modify existing software, as well as for potentially developing new diagnostics and
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models. This framework should be engineered to simulate a range of attack scenarios with high fidelity (i.e. exploitation of network and device vulnerabilities). Abertay University possesses a mature, well
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, management, quantitative analysis Lead in publication outputs in appropriate academic and industry fora. The role-holder will also have management, liaison and networking responsibilities. The skills
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that define building leakage characteristics. Surrogate Model Development: You will develop data-driven surrogate models capable of estimating air leakage in unseen building types. These models will be trained
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an advantage. The successful applicant will carry out research activities in the domain described earlier and will disseminate research outputs through scientific publications, software development, seminars and
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development over the last two decades. This research topic aims to define novel approaches to developing and combining these intelligences, utilizing both 1st and 2nd wave AI approaches, in the context
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into full-surface temperature profiles. Key outcomes include defining the required locations and extent of temperature monitoring to enable accurate data conversion. • Creating a practically deployable method