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Field
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(DC15): Enhancing water distribution management through District Information Areas: A data-driven approach to optimisation and automation The successful candidate will develop the concept of District
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: Undergraduate degree, MSc or PhD (desirable) in EE, CS, relevant engineering Knowledge and experience ML/AI for optimisation Knowledge and/or experience of O-RAN, relevant wireless standards and testbed Ability
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-related discipline. 2) Strong programming ability in optimisation or machine learning (e.g., Python/Matlab/C++; PyTorch/TensorFlow). Experience in signal processing/wireless or SDR/GPU prototyping is a plus
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(2026) https://doi.org/10.1016/j.cep.2026.110813 The role You will develop flow-based ultrasonic systems for applications selected by impact, in terms of sustainability. Substantial optimisation
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is responsible for developing end-to-end data and AI/ML workflows for the medical diagnostic platform, from device data acquisition and signal/data preprocessing to model optimisation, validation
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electrical and structural mechanisms driving arrhythmia in individual patients. This project will develop a patient-specific cardiovascular digital twin to support the planning and optimisation of AF ablation
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complexity theory, quantum algorithms, optimisation and quantum sensing. Your work will contribute to the development of new computational frameworks and approaches spanning sensing optimisation, adaptive
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accurate quantification of disasters using remote sensing and space geodesy. They will also advance InSAR processing algorithms to optimise deformation mapping and change detection capability in Southeast
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efficient navigation algorithms for multirotor operations around vessels. The role will focus on integrating drone airspace-usage predictions with Vessel Protected Volume models, developing optimisation and
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of two-dimensional materials and van der Waals heterostructures. Design, build, optimise, and operate advanced optical spectroscopy experiments using techniques appropriate to the research programme