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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 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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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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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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of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the MR signal into the training of the INR network, we aim to compensate for the effects
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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
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to investigate the potential of using Implicit Neural Representation (INR), a class of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the
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experience or strong interest in power system modeling, optimization, machine learning, and control systems. Documented programming experience (e.g., GitHub projects) in Python, Julia, MATLAB, or similar