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. The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned
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distortion that arises in a complex aero-engine intake. This is predominately a hands-on experimental aerodynamics project with the goal of developing an approach to synchronously acquire velocity and total
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investigate strategies to enhance communication security, focusing on resilience against jamming and spoofing attacks. Students will work on designing secure architectures that ensure data integrity and system
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intelligence, multi-agent systems, and the design of AI models. They will also acquire transferable skills in interdisciplinary problem-solving and innovation, which will significantly enhance
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systems that continuously assess the health of components, predicting failures before they occur. Compliance Assurance Techniques: Design AI-driven methods to ensure ongoing compliance with industry
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
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sectors like aerospace, healthcare, and manufacturing. The convergence of AI with fault-tolerant design principles is transforming traditional maintenance paradigms, leading to more robust and intelligent
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
both industry and academia. They will develop advanced expertise in experimental design, composite materials testing, and non-destructive evaluation, alongside practical experience in machine learning
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-based method to approximate the CFD-revealed effects of liquid metal convection on molten pool temperature predictions. • Designing and conducting instrumented WA-DED experiments to validate the developed