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that pose a health and safety risk within your area of responsibility Key Selection Criteria: Qualifications: Degree in Information Technology, Computer Engineering, or a related field, or equivalent level of
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Levin Kuhlmann Research area Machine Learning We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and
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and standing recognised by the University/profession as appropriate for the relevant discipline area (e.g., AI/Machine Learning, Bioinformatics). A proven track record of research and scholarly
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mechanical loading of such samples. The focus of the PhD project will be to use machine learning techniques to better understand the interplay between the crystal orientations and deformation patterns in a
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Machine Learning without Centralized Training Data”, https://ai.googleblog.com/2017/04/federated-learning-collaborative.html [2] “Learning with Privacy at Scale”, https://machinelearning.apple.com/research
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the mobility data and design optimisation and machine learning algorithms Validate the design on energy-transport simulation platforms Present and promote research findings in international conferences and