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mechanics, or advanced material behavior. Programming skills (e.g., Python, MATLAB, Julia,..), ideally with experience in numerical methods or scientific computing. Familiarity with machine learning
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selection of relevant harmonics. While frequency methods are oftentimes considered more efficient than numerical time integration methods, the computational cost and complexity increases dramatically when a
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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within the space between the grains). - Simulating mechanical behavior using, e.g., finite element methods. - Measuring mechanical performance at the sample scale. - Investigating mechanical behavior
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comparative insights that enhance research conclusions from Hope observations. Develop Machine Learning methods and run numerical simulations on NYUAD’s High-Performance Computing (HPC) system. Support
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Grant(s) (RG) in the scope of R&D projects FireLSF - Development of predictive models for the fire resistance of light steel frame walls - an integrated experimental, numerical and machine learning
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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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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through measurements in an acoustic tank on plate and cylinder models. - Development of analytical and numerical methods to describe the coupling of vibroacoustics in heavy fluids and thin structures
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(parallelization, efficient data structures), numerical testing, and results analysis. Familiarity with numerical methods, scientific programming in C++, and an interest in reservoir engineering problems