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, and interpretation of large DAS and seismic datasets. Integrate distributed sensing observations with complementary geophysical and environmental data to improve urban subsurface imaging and monitoring
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integrates clinical, molecular, and real-world data with emerging computational approaches, including large language models, predictive modelling, and multi-omics data integration. The lab coordinates and
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to study how dysprosium’s large magnetic interactions can be utilised in future magnetic field sensors and to investigate precursors for novel quantum phases of matter. The project is supported by Quantum
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maritime networks from the sixteenth to the nineteenth centuries. It will bring together large quantities of previously fragmented archival evidence from across Europe to reconstruct patterns of maritime
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timely research question: How can Large Language Models (LLMs) and intelligent agents support transparent, scalable, and auditable clinical data harmonization? We are particularly interested in: LLM-driven
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excellence in all its dimensions: Searching for fundamental results and insights, developing novel computational solutions to a wide range of applications, building large-scale experimental systems and
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programming skills. Documented experience in big data analysis. Documented experience in high-throughput sequencing data analysis. Language requirement: Good written and oral English proficiency. English
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. The successful candidate will develop machine learning models for large-scale biological data and connect predictive modeling with mechanistic understanding. As part of Karsten Borgwardt's Department of Machine
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longitudinal data analysis Contribute to the writing of research papers and reports Manage large research dataset Other duties as assigned by the Principal Investigator Application Applicants (external and
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methods, data mining, and machine learning methods. Documented shell script, R, Python, and C programming skills. Documented experience in big data analysis and computational tool/package development