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integrating GIS data, IoT sensor measurements, and domain-specific information. AI models will be developed to predict dynamic behaviors and key risk zones, and these models will be embedded into the digital
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integrating GIS data, IoT sensor measurements, and domain-specific information. AI models will be developed to predict dynamic behaviors and key risk zones, and these models will be embedded into the digital
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PhD Studentship: AI Optimisation of Green Roofs and Facades for Low-Carbon, Heat-Resilient Buildings
, together with Python programming experience and experience in artificial intelligence or machine learning. They should also have experience in one or more of the following areas: GIS or remote
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, hydrology, physics‑based modelling, statistics, or remote sensing. Experience with Python, scientific computing, or geospatial data tools (e.g., GIS, satellite imagery processing) will help you hit the
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, hydrology, physics‑based modelling, statistics, or remote sensing. Experience with Python, scientific computing, or geospatial data tools (e.g., GIS, satellite imagery processing) will help you hit the
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. Technical Skills Proficiency in remote sensing (satellite image processing, GIS, photogrammetry); Programming skills for geospatial analysis: Python (rasterio, geopandas, scikit-learn) or R
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Qualifications Proficiency in statistical software (R, Python, etc.), and working knowledge of data management protocols. Experience with exposome or environmental data, multiomics integration, and
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cycling, soil carbon, biomethane, fertilizer use, and feed and food production; and evaluate economic and wider socio-economic benefits, drawbacks and trade-offs. The project combines GIS and
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#, F#, .NET, TypeScript, Angular, JavaScript, HTML5, CSS, SQL, Python, and related technologies. • Designing and implementing scalable software architectures for modern web-based applications
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datasets. Proficiency in statistical software (R, Python, etc.), and working knowledge of data management protocols. Experience with exposome or environmental data, multiomics integration, and exposure