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: Machine Learning Molecular Dynamics. The project involves the development and application of machine learning methods that enable a major boost of the time and length scales accessible to ab-initio/first
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-matter interactions, via theory and simulations, to measure quantum dynamics in atoms and molecules. The project aims to explore new ideas for how quantum tomography, combining multiple measurements and
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provide powerful tools to improve the quality and efficiency of data-driven models. In parallel to the development of data-driven models for dynamical systems with geometric structures such as Hamiltonian
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that corrected pseudoranges correspond to physically consistent receiver positions across all satellites. Temporal smoothness: enforcing corrections that are consistent with expected receiver dynamics, such as
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, the construction of the geological model remains a significant challenge. The geological model is key because it constrains the volumes and data used when inferring the mineral resources, which in turn inform
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The emergence of 3D concrete printing (3DCP) is reshaping the future of construction, offering unprecedented design freedom, material efficiency, and automation. Unlike conventional concrete casting
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knowledge gaps. The project involves both linear and nonlinear dynamics modelling and analysis, as well as experimental testing. An equivalent test structure will first be constructed in the vibration
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structure interaction methods and/or wind tunnel experiments to understand and quantify the aerodynamic and aeroelastic effects caused by rotors operating in close proximity to each other’s. Understand what
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the interaction between the structure of CFM and dynamic performance of the flow. The aim will be achieved through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect
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and mobile genetic elements in waterways. High-throughput sequencing approaches such as metagenomic sequencing will be used to study the microbial community structure, resistome diversity, and potential