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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
predictive and explainable digital twins. The core challenge this PhD will tackle is how to help digital twins make sense of complex, messy maintenance data and turn it into clear, useful insights
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novel sensing approaches to combine with machine learning algorithms to solve real-world problems in food manufacturing. You will have sound knowledge in electronic engineering, embedded systems design
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open to both Home and International students (though international students would be subject to paying the difference between home and international student fees). For more information, please feel free
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and deliver evaluation and learning approaches that help places make sense of how and why things work. This PhD aims to develop and apply qualitative methodologies that can be more effectively included
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ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others in our community.
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inclusion and encourages applications from all sections of society. The Faculty of Engineering (FoE) provides a thriving working environment for all Postgraduate Researchers (PGRs) creating a strong sense of
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disruptive aircraft configurations involves combining advanced engineering practices, including computing power, sensing, AI/ML, and system-level engineering. Comprehensive verification and validation
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invite suitable candidates to interview already during the application period. We aim to have a transparent and equal recruitment process, so feel free to ask us for feedback. Aalto University reserves
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use including increasing urbanisation are driving factors in biodiversity decline worldwide. While digital technologies are changing our cities through networked sensing, AI and cloud computing in what
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is primary adhesion of bacteria to solid surfaces mediated? How do bacteria sense surface attachment and switch from a planktonic to a sessile (attached) physiology? How do sessile bacteria coordinate