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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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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
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explainable physics-informed RNNs for autonomous navigation and neural observer design within the broader framework of Embodied AI. The goal is to integrate physical models with deep learning to create
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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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Imagine a world where food production is in harmony with natural processes, farmers nurture healthy soils, and biodiversity thrives. In contrast, current monoculture farming systems undermine
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models for forest-based 3D point cloud data. In recent years, large advances have been made for deep learning algorithms for high-resolution point clouds from small geographic areas. We seek a candidate