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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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conduct unique research and design machine learning algorithms for detecting relevant activities from multiple cameras, and possibly other sensors as well. Would you like to work closely with
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& infrastructure modelling, algorithmic decision-making, transportation engineering, uncertainty quantification, geo-data analytics, machine learning, and optimization. Experience with data-driven and/or deep