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programming framework called Tiramisu [1]. Unlike existing frameworks, Tiramisu can perform advanced code optimizations that are hard to apply otherwise. Because of this, Tiramisu can generate fast code
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at the contact scale, i.e., between two grains connected by a solid bridge formed by the solidified foam. These studies will provide a fundamental understanding of the hybrid material and allow its optimization
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monitoring. • Develop numerical models to simulate the dynamic behavior of mooring and anchoring systems under different environmental conditions. • Analyze and optimize structural performance and predictive
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biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run high-performance numerical experiments
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by desorption, as well as crystallization and precipitation of Magnesium Ammonium Phosphate (MAP). These processes are tested and optimized on a pilot-scale wastewater treatment plant using real
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian We are looking for a Research Associate to conduct numerical modelling
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the BERNARDO project, funded by the PEPR LUMA program (https://www.pepr-luma.fr/ ). The project focuses on chiroptical interactions, which play an essential role in fields such as biomolecular recognition
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advanced research in structural health monitoring, including hardware development. Perform numerical studies and model testing. Analyze and interpret data to provide recommendations for system design and
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institutions, and a research and development provider for numerous companies throughout the world. The INM is a member of the Leibniz Association and has about 250 employees. The INM Energy Materials Group
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for the captioned post. Duties and Responsibilities Conduct advanced research in structural health monitoring, including hardware development. Perform numerical studies and model testing. Analyze and interpret data