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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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Position Description The School of Mathematics offers a few post-doctoral positions every year. These are research positions in Mathematics, tenable for one year in the first instance and renewable
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(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and
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scattering, computational imaging, and intelligent sensing. Develop novel theories, methodologies, and technologies for high-resolution imaging, information reconstruction, and target characterization in
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Architecture: Architect, build, and evaluate multi-agent systems powered by LLMs and machine learning, incorporating Orchestrator and Memory agents, vector databases, and knowledge graphs to enable complex goal
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on evolving surfaces, and seeks to construct appropriate numerical schemes and their demonstrate their convergence. About you The successful candidate will hold a PhD in Mathematics or a closely related area
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of behavioural models for dynamic analysis. Particular emphasis will be placed on the development of temporal modelling concepts, knowledge graphs, and formal verification techniques, enabling the automatic
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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-ranked international journals. Qualifications and personal qualities: Applicants must hold a master's degree or equivalent education in engineering, informatics, mathematics or physics, or have submitted
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anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and simulation Demonstrated Applied AI for Healthcare and