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turbine blades. Successful re-development for end-of-life composites could enable reuse in other structural applications. This PhD will investigate the development of hierarchical Bayesian algorithms
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Language Processing (NLP) and correlation algorithms applied to interaction data, metadata, and multimedia content, it ensures information integrity for both legal and regulatory compliance and the execution
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. With the support of machine learning algorithms and log analysis applied to traffic metadata and communication flows, it ensures system resilience for both legal and regulatory compliance as
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automation, programming, scripting languages such as Python, and algorithm development. You will have extensive experience of software development / PhD in Computing or in Chemistry with a strong computing
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will develop and evaluate fault detection and fault location algorithms for these systems. The project is funded by GE Vernova under a wider collaboration with Imperial College London. You will be co
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project team regularly use for the production of model colloidal films, ceramic dielectrics, photovoltaics and battery electrodes to provide the datasets required to educate the machine learning algorithms
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oversee and develop algorithms for analyzing ensemble genomics data, single cell genomics data, single cell merFISH and sequential oligopaints imaging data, as well as novel molecular connectomics data
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for Artificial Intelligence. The position aims to strengthen methodological and algorithmic work on one or more of the following axes: Optimization and learning on very large models and datasets, improving
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Doctoral Candidates (DC1 and DC2) to carry out research in neuromorphic photonic-electronic integrated circuits for brain-inspired information processing and sensing (DC1) and in the development of efficient
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methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data modalities. The candidate will have the opportunity to work