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Field
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aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
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aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
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to generate bespoke algorithms for advanced image analysis. This will typically require coding expertise in languages such as MATLAB, R and/or Python; Proven ability to develop constructive working
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and accurate registration of ultrasound scans of 3D-printed human skulls to MRI/CT head scans. The research associate will develop anthropomorphic head phantoms and algorithms for fast and accurate
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experimental research as well as data analysis and algorithm development. Students with either an undergraduate honours degree (1st) or MSc (Merit or Distinction) in engineering, mathematics, neuroscience
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are critical especially around congested or critical infrastructures. This research aims to develop decision making and planning algorithms that can mitigate the risks challenging environments of AAM
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are not limited to: network architecture design for NTN and terrestrial network (TN) convergence, intelligent traffic steering algorithms between TN and NTN, orchestration of TN/NTN resources for end-to-end
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including, but not limited to Computational Thinking, Algorithms, Data Structures, and Software Development and will have supervised dissertations at both undergraduate and postgraduate levels. The post
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transmission methods (wired or wireless) will be optimised for robust data capture in natural sleep environments. AI-Driven Analysis: Develop advanced AI algorithms to analyse the collected sensor data, aiming
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the best performance from the CCD during the SMILE mission lifetime. Support the analysis of the SMILE CCD radiation damage studies and support development of radiation correction algorithms for the SMILE