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, including situations where some modalities are incomplete or unavailable. Exploring foundation-model and self-supervised learning approaches for extracting transferable representations from large-scale forest
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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(WEFE) nexus, and model-based learning and communication. The group makes use of a variety of tools and techniques: stakeholder mapping, governance analysis, participatory modeling, non-linear feedback
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for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high-dimensional data. Develop modular
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leader is Associate Professor Soledad Gonzalo Cogno. About the project The successful candidate will contribute to the development of mathematical and computational models to enquire about the mechanisms
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benchmark chemometric and physics-informed machine learning models to monitor, forecast, and ultimately control critical process parameters, implanting these models in advanced control frameworks to optimize
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renewable energy use, energy security, and the reliable operation of hydro-dominated power systems. The project will focus on how AI can support advanced optimization models for hydropower and energy-system
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learning models for segmenting and interpreting forest point clouds and rebuild them as real-time, incremental estimation systems. The work supports navigation, self-localization, and traversability work
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when using closed, proprietary models, where model weights, training data, and internal representations are inaccessible. The PhD project will therefore investigate how trustworthy agentic AI systems can
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for identifying concentration, floe size, geometry, and possibly stage of development. The plan is to build models so that radar measurements alone can be used to populate, as far as possible, the Stage