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systems. You will support digital project delivery and the University's Smart Campus initiatives, integrating spatial and sensor data to enhance building performance, space use, and user experience. From
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development. The main tasks will be in photometric light curve analysis and validation (Gaia and PLATO) and data analysis and development of novel scientific algorithms and applications in the areas
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, model compliance, and integration with operational systems. You will support digital project delivery and the University's Smart Campus initiatives, integrating spatial and sensor data to enhance building
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proofs obtained via state-of-the-art theorem provers and model-checkers. There will be a need to develop novel synthesis algorithms that can scale. There will be a need to fine-tune foundational models
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learning and generative AI approaches, creating synthetic virtual patient cohorts from multimodal datasets. Your work will involve designing advanced algorithms and high-throughput workflows for crafting
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deep learning and generative AI approaches, creating synthetic virtual patient cohorts from multimodal datasets. Your work will involve designing advanced algorithms and high-throughput workflows
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of such systems, but this is driven from the fundamental theoretical knowledge that underpin them. You will be co-creating new models, algorithms and schemes to advance the field of joint sensing and communications
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Health Monitoring (SHM), Structural Dynamics, Sensor Integration and Processing, Internet of Things (IoT), Machine Learning (ML), Predictive Maintenance This is a 30-month Knowledge Transfer Partnership
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graduate to work full time on a 2-year Knowledge Transfer Partnership (KTP) to develop an advanced Multi-Sensor Condition Monitoring System (MSCMS) for enhancing the Offshore Wind substation Platforms
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an advanced Multi-Sensor Condition Monitoring System (MSCMS) for enhancing the Offshore Wind substation Platforms through a multi-sensing environment with reliable on-shore data communication. Employed and