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In this project, we aim to pioneer foundational models specifically designed for time series data—a critical step forward in handling vast and complex temporal datasets generated across domains like
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Synthetic data generation has drawn growing attention due to the lack of training data in many application domains. It is useful for privacy-concerned applications, e.g. digital health applications
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Automating code generation, SQL query formulation, and data preprocessing pipelines is a crucial step toward intelligent and efficient software development. This project aims to leverage large
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Research data governance is an under-explored issue, and technical infrastructures to support the transparency and control of data collected in human research studies (from medicine to social
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This PhD project focuses on the design and evaluation of hybrid quantum–classical algorithms for large-scale data analytics and optimisation problems. The research will investigate how quantum
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-quality spectral data from wet-lab experiments is expensive and time-consuming. Furthermore, relying on a single spectral modality often leads to ambiguous generation, as different molecules can yield
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Many machine learning (ML) approaches have been applied to biomedical data but without substantial applications due to the poor interpretability of models. Although ML approaches have shown
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to examine vast amounts of textual data, identifying keywords, phrases, and sentiment that may indicate extremist views or intentions. Analysing audio involves techniques such as speech recognition, keyword
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the executive search firm SHK. To apply, please submit your application via the link provided at the bottom of this advertisement. For more information prior to application, please email Siobhan Forbes at SHK
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This project concerns the investigation of suitable socio-technical data infrastructure for law-enforcement research and development. International collaboration between law-enforcement agencies