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-science and signal-analysis tasks, including processing experimental signals, integrating datasets, developing machine-learning models, and mapping measured fuel properties to SAF performance. For the post
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, preference will be given to those with strong Python/programming and research skills, and experience in graphs/ontologies, machine learning, optimisation/control, time-series forecasting, BMS/HVAC, PV or ESS
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- “Reliable industrial foundation models for trustworthy industrial robot fault diagnosis in automotive smart manufacturing”. He/She will be required to: (a) carry out research in advanced machine learning
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) experience with non-destructive testing (NDT) methods (e.g. ultrasonic, thermography, eddy current); (c) knowledge of automation, control systems, and sensor integration; (d) familiarity with AI/machine
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analysis and application of machine learning techniques to structured numerical and unstructured textual data; (d) have a good track record of academic writing, including report writing, manuscript
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publications at major machine learning, AI or closely related conferences and journals; and (b) good communication and writing skills in English. Applicants are invited to contact Prof. Qiu Anqi via email at