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360 Algorithm Design F 2025 LOCATION Downtown Campus Schedule TR
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on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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. This is part of an EPSRC-funded project on Algorithmic Comparison of Stochastic Systems. The post holder will work closely with the Principal Investigator, Stefan Kiefer, but also with other members of a
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learning algorithms for a variety of predictive analytics research projects. Coordinates data collection, econometric analysis and provides quality assurance for research projects. Contributes to research
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. The position is for one year, renewable subject to satisfactory performance. Successful candidates will conduct research and develop advanced deep learning and computer vision algorithms. Candidates are expected
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on the robustness (continuity) of equivalences in probabilistic systems. This is part of an EPSRC-funded project on Algorithmic Comparison of Stochastic Systems. The post holder will work closely with the Principal
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algorithms in extremely complex and enormously large spaces motivated by physics and chemistry Developing interpretable AI for scientific discovery in physics (example here ) Formal mathematics (using Lean’s
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. Integrate various datasets, such as tree species annotations, climate, and topography, into deep learning algorithms. Test deep learning models (Transformers and CNNs) for optimal accuracy using large
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and testing software in high level languages; writing and testing logic in hardware description languages; developing and testing signal processing algorithms from concept to implementation; and