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Field
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position will contribute to the research programme of the recently founded "AI Hub in Generative Models", a research consortium funded by EPSRC. The goal of the programme is to do research in the area of
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position will contribute to the research programme of the recently founded "AI Hub in Generative Models", a research consortium funded by EPSRC. The goal of the programme is to do research in the area of
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approach including empirical data analysis, experiments, and theoretical modelling to develop science-based management strategies for the restoration of woodland ecosystems. We will collect, and collate from
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enrichment strategies can be tailored for best conservation success. This PhD project will combine empirical fieldwork with cutting-edge spatio-temporal ecological modelling of systems dynamics to investigate
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limitations in both measurement and modelling techniques. Current in-process measurement methods are restricted to surface-only monitoring devices (e.g., cameras and pyrometers), which fail to capture
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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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machine-learning surrogate models capable of delivering near-DFT (density functional theory) accuracy in just a few CPU seconds per structure. This approach will enable the high-throughput screening of tens
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energy data lifecycle spans pre-construction (e.g., meteorological mast data, LiDAR data, wind climate and energy yield modelling, environmental impact assessment data), operational phases (e.g., SCADA
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heavier than their fossil fuel powered counterparts. A framework that can accurately model complex dynamics and generate projections for future scenarios is essential for understanding the impact of changes
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Samuele Lo Piano, samuele.lo.piano@pg.edu.pl , Politechnica Gdanska s.lopiano@reading.ac.uk , University of Reading Project description: Optical radiative transfer models (e.g. canopy reflectance models