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in the Sheffield Algebraic Geometry and Mathematical Physics group (https://agmp.sites.sheffield.ac.uk/ ) within the broader School of Mathematical and Physical Sciences at the University of Sheffield
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models. Knowledge base of multivariable and vector calculus, as well as geometric algebra. Familiarity with machine learning and algorithms. Knowledge and proficiency with protein Large Language Models
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg
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charged GAGs which, when combined with collagen, produce an osmotic effect. The fluid-structure interaction will be modelled as a homogenised continuous medium within the framework of poromechanics, while
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. The study will include a detailed evaluation of geometric and material parameters influencing charging and discharging performance to identify configurations that maximise energy efficiency and heat-transfer
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., VAEs, GANs, diffusion models) or graph/equivariant neural networks for materials (e.g., CGCNN, SchNet, NequIP), and related tooling (e.g., PyTorch Geometric, pymatgen, ASE) is beneficial but not required
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . We are looking for a Research Associate to develop novel AI-based generative design methods for heat exchanger
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of the following fields: algebra, algebraic geometry, differential geometry, algebraic topology, complex geometry, quantum topology, geometric analysis, operator algebra, number theory, mathematical aspects
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(Podostemaceae) in Neotropical rivers migrated and evolved as the Isthmus of Panama formed and develop the first model for the tempo and pattern of formation of riverine connections across the Isthmus
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historical data. You will evaluate the performance of these models on real industrial cases in order to propose a demonstrator or prototype that can be used in production. Where to apply Website https