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models, making the use of data-driven approaches a promising direction. This PhD project will investigate the use of data-driven and machine learning approaches, both measurement based but also model based
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MEng degree (or equivalent) and a PhD in Maritime Engineering and Technology or pertinent disciplines (Res Assistant if no PhD), adequate knowledge of modelling marine engines operations with alternative
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performance through physics-driven insulation material design. Applicants should hold a first-class (or equivalent) degree in a relevant engineering or science discipline (upper second class may be considered
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, economic viability, and robustness to realistic operational uncertainty. PhD (or equivalent) in control engineering or closely related discipline. Track record in at least two areas: model predictive control
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renewable energy, AI-driven engineering, and industrial research. Cranfield’s expertise in wind energy systems, predictive maintenance, and AI applications provides an ideal environment for cutting-edge
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Development of novel processing techniques Modelling techniques that can inform the direction of experimental activity Physical, mechanical and materials characterisation techniques Data-driven approaches
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Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers
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comprehensive analysis of the extensive Pulse dataset, uncovering latent patterns and taxonomies that define building leakage characteristics. Surrogate Model Development: You will develop data-driven surrogate
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Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers
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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