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. Assess the build quality of parts generated through control model algorithms. Validate that methodologies developed are transferrable between different LPBF platforms through evaluation of parts generated
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processing, data analysis, data-driven modelling, optimisation and computation algorithms, machine learning models and neural network structures, as well as strong skills and experiences in computational
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decision with multiple data sources. One example is to develop the semi-supervised methods and dynamic system interfacing algorithms to produce an automated and real-time information exchange across
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hardware-in-the-loop (HiL) techniques and ML algorithms for the accurate and on-time detection of faults, so that failures can be prevented by alerting the end-users and diagnosticians during periodical
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manipulation strategies. Develop intelligent algorithms for the robotic execution of contact-rich manipulation tasks, enhancing adaptability, precision, and efficiency in a manufacturing context. Collaborate
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the project. Plan your own research schedule and workload within the project schedule to ensure that the overall project milestones and objectives are met. Liaise closely with other academics, advisers and
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infrastructure and data. Your responsibilities will encompass planning, scheduling, and delivering engagement, governance, and data management activities. You'll facilitate agile workflows, including daily stand
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obstacles to optimise team efficiency. Your responsibilities will encompass planning, scheduling, and delivering engagement, governance, and data management activities. You'll facilitate agile workflows
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/adaptive algorithms, offline and online data analysis, conducting experimental research, and online evaluation of the developed adaptive strategies with a robotic application. The prospective students can
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pages and campaign landing pages, ensuring they are up-to-date, inclusive, user-friendly, and optimised for search engines. Assists with social media activities, including content creation, scheduling