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Cartographic Heritage to model the impact of land changes on the hydrological and river systems in Europe The main goal of this proposal is to develop strong research and analysis skills of a PhD student
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fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content The PhD programme offers advanced studies in theoretical and
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special focus on energy micropiles. The Geotechnical Engineering Group and is well known for its work in the field of quick clays, slope stability, energy geostructures and advanced numerical methods
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biosynthetic gene clusters and other molecular features relevant to natural product drug discovery Validating machine-learning and deep-learning models to predict the chemical structures and bioactivity
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analysis will be used to study the seismic response of prototype structures for different scenarios. Hence, the project integrates advanced element-scale, model-scale and in-situ testing with state
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: Full Time Closes: 7 January2026, 23:59 GMT Application link: https://www.essex.ac.uk/postgraduate/research/doctoral-training-partnerships/aries ARIES (Advanced Research and Innovation in
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-water settings. The research will develop a unified framework that fuses heterogeneous sensing modalities through uncertainty-aware probabilistic optimization while maintaining semantic, structural, and
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transport, microbial systems, or circular bioprocesses. You will contribute to developing and applying novel modeling strategies, AI-enhanced simulations, and computational workflows to explore biological
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environment. Apply now if you are motivated to drive the project and eager to advance applied forest remote sensing. Main tasks Process remotely sensed data Develop statistical models predicting tree- and
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the broader framework of Embodied AI. The goal is to integrate physical models with deep learning to create interpretable, data-driven observers that enable physically grounded perception and control for robust