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Field
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simulations run on world-class supercomputing facilities alongside AI algorithms and data analytics tools, and share your results with experimental collaborators. The position is part of the Research Council
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subject from a recognised institution. A background in at least one of the following: Dynamical systems Photonics/Electromagnetics theory, design and simulations Machine learning mathematics and algorithms
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, PyTorch), Machine-learning mathematics and algorithms. Experience in Imaging systems (e.g. microscopy), and optical laboratory experiments (lasers/lenses) Funding and eligibility Open to UK, EU and
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codes and process their results. Helping to develop new models and algorithms to simulate pulse propagation, the material response, and other aspects of our experiments. Coding in Julia and python. Take a
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probability, statistics, and algorithms. Demonstrated interest and capability in supporting research and academic project work, with the ability to develop software solutions to technical problems. Strong
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active research streams within the group. They will consider the use of cutting-edge deep learning algorithms for object detection and tracking, anomaly detection and other generalized data understanding
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algorithms and simulation models using Python, MATLAB, Julia, or other suitable tools. Analysing simulation and project data, and validating the developed methods through practical case studies. Preparing
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also be involved in improving object reconstruction, such as developing advanced algorithms for tau lepton identification or jet substructure / particle-flow techniques to identify Higgs bosons decaying
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on principles of diffuse optics. Developing algorithms to categorise the above in specific medical use cases. Manufacturing diffuse optical phantoms as a parallel to clinical models, Performing rigorous and
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& automata theory (including advanced algorithms, formal methods), and foundations of machine learning (including linear algebra and calculus). The ability to apply and generate externally funded research