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Field
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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engineering problems is highly desirable. Proficiency in programming languages such as Python, MATLAB, or R. Experience with digital tools for 3D modelling, GIS, or drone-based mapping. Ability to analyse
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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or compromised IoT devices by analysing encrypted traffic patterns, focusing on metadata, flow characteristics, and timing rather than decrypting payloads. The core challenge is creating features and models
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challenge-driven with a systems-based approach and requires interdisciplinary efforts, which is reflected in our team's composition spanning engineering, natural and social sciences. It is a dynamic and
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academic background, successful candidates should have experience in one or more of the following: Experience of data-driven modelling and optimization-based analysis. Knowledge of fluid mechanics. Knowledge
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Australian Research Council (ARC) Funded PhD Opportunity at Faculty of Engineering: High-Speed Rail and Sustainable City Sizes in Australia Location: Clayton campus Department/Unit: Monash Institute
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diverse backgrounds (e.g., economics, engineering, computer science, information systems, etc.), united in pursuit of sustainable solutions that positively impact and shape a low-carbon economy and society
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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3rd August 2025 Languages English English English The Department of Chemical Engineering has a vacancy for a PhD Candidate in process modelling, simulation and feasibility studies Apply for this job