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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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the provision of support to student projects. · Experience in supply chain network design and logistic optimization. · Ability to contribute to the planning and management of independent research.
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discrimination. You will also contribute to the implementation and optimization of machine learning and deep learning models, including DNNs, CNNs, and RNNs, enhancing the performance of our sensing system by
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include the modification of existing optimization procedures to explore the potential energy surface of materials in search of transition states and structural transformations using existing and newly
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been shown to accelerate and improve the training procedure of SNNs by defining new cost functions that are differentiable and easier to optimize. They can also handle quantized weights, e.g., using
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prototype (MIDAS) that integrates AI-based modules with optimisation engines to support low-carbon, cost-optimal datacentre microgrid design. To manage prototyping of the software platform - overseeing build