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characterisations and advanced characterisations such as X-ray, electron, and neutron scattering and imaging experiments. You will undertake independent and collaborative research and will be expected to write up
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, quality-diversity optimisation, constrained optimisation Genetic programming, code evolution, neuroevolution Fitness landscape analysis and visualisation Machine learning for algorithm performance
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medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and astrostatistics. These posts
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are included but clinical medical themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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two-photon calcium imaging and/or Neuropixels electrophysiology. A strong background in analytical and/or statistical analysis, along with proficient programming and coding skills, is essential
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will have experience with zebrafish models, light stimulus design, hyperspectral imaging, and behavioural monitoring. You will be adept in coding and analysis using tools such as MATLAB and Python