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Field
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, machine learning, and information-theoretic approaches to achieve robust, non-intrusive security for the ever-expanding IoT landscape. Feature Engineering for Encrypted Traffic: It is crucial to identify
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behaviours? The proposed approach will focus on developing a multi-agent AI framework that integrates traditional penetration testing methodologies with machine learning techniques and advanced generative AI
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: SIMD performance engineering. Machine Learning. Communication-efficient
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Analysis . We conduct research to find more sustainable technology solutions and ways to transform technological systems to meet the environmental and resource constraints faced by society. Our work is
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Failure Analysis of Composite Sleeves for Surface Permanent Magnet Electrical Machines This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites
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learning. Supervisor: Prof. Udo Bach, Department of Chemical and Biological Engineering. (Email: udo.bach@monash.edu ) Manipulating light at the nanoscale Supervisor: Dr Alison Funston, School of Chemistry
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This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable
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Research Back Profile Areas Cluster of Excellence CMFI Cluster of Excellence GreenRobust Cluster of Excellence HUMAN ORIGINS Cluster of Excellence iFIT Cluster of Excellence Machine Learning Cluster
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Closing Date: 15 August 2025 Eligibility: UK Only Funding: Joint School of Civil Engineering/EPSRC Doctoral Landscape Award Studentship, providing the award of full academic fees, together with a