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We are seeking for a highly-skilled and self-motivated candidate with a strong mathematical background to do a Ph.D. on the fundamental aspects of graph machine learning with applications
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simulation models and enriched by operational equipment performance data. To this end, physics informed machine learning techniques will be used to bring model data and real data together in a Digital Twin
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will explore the use of cutting-edge scientific machine learning framework that blends deep learning with physics-based techniques to achieve the goals of (i) identifying precursors and mechanisms
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Challenge: Analyse properties of biological systems Change: Develop novel control theory and machine learning methods to study natural systems and their robustness Impact: Produce new intelligent
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Challenge: Solve computational bottlenecks in the modelling of mechanics of metallic systems. Change: Develop new physics-informed machine learning algorithms and predictive models. Impact: Enable
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Challenge: Generating realistic bathymetric maps at a large scale using satellite images and advanced machine learning methods. Change: Incorporating physics into satellite-derived bathymetry
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Explore and develop scalable machine learning methods for aerospace structures' design and manufacturing This postdoctoral research aims at exploring statistical methods that enable the data-driven
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. Machine learning and artificial intelligence, currently revolutionizing the fluid dynamics field, can be powerful tools in such cases. However, they remain limited to simple scenarios involving single-phase
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Join us on our quest to overcome a long-standing research challenge in soft tissue biomechanics through the combination of multi-modal experimental tissue testing data, machine learning and physics
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of our department. You will learn how we setup and maintain them. You will learn how the machines are used by our researchers. What software is used for IC design and Deep learning. As your expertise grows