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, the internal workings of deep neural networks remain largely mysterious, posing a significant challenge to the interpretability, reliability, and further advancement of these models. This project seeks deep
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My research interests focus on the stars - primarily their structure, evolution and nucleosynthesis. This can involve modelling of mixing in stars, or effects of changing nuclear burning rates
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the development of numerical methods for astorphysical fluid dynamics and radiation transport. Projects may employ a range of approaches from analytic modelling and numerical calculations on desktop
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I supervise a wide range of projects stellar astronomy. They include modelling stars in 1D or 3D, deciphering the origin of the elements (stellar nucleosynthesis), and observing using optical
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interesting implications for standard models of hierarchical black hole growth. Aims This project aims to explore the optical/IR properties of the most extreme IFRSs, and confirm their redshift range
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background and/or previous experience or publications in materials science, chemistry, engineering physics, nanotechnology. Experience or strong interest in modelling, and/or clean energy. Preferable
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Inference Tool (GAMBIT) Community I study various theoretical frameworks that extend the standard models of the elementary particles and cosmology to understand the nature of dark matter, dark forces and dark
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organic nanomaterials for future electronics, optoelectronics and spintronics" "Light-transformed materials" "Theoretical and numerical modelling of the electronic structure of functional low-dimensional
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of mesons and baryons and their role as indirect probes for physics beyond the standard model. I also follow searches for new physics at the large hadron collider (LHC) and use them to constrain new particles
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models that can forecast the likely outcomes of current practices. The project aims to develop cutting-edge machine learning and statistical risk prediction techniques to predict each short-term, long-term