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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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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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discipline. Strong background in heat transfer and thermal transport modelling. Experience with numerical methods for transport equations (e.g. BTE, kinetic methods, finite-volume / finite-difference
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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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at companies or the public sector. 5.2 — The score obtained in the curricular evaluation method is expressed on a numerical scale from 0 to 20, considering the valuation to the nearest hundredth. 5.3 — The jury
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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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these inputs for device-scale structures, with methods such as DFT, currently poses a bottleneck in the application's capabilities. Project background The Computational Nanoelectronics Group was recently awarded
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, statistical thermodynamics, and numerical methods for partial differential equations. Experience or a strong interest in the study of stochastic fluid models is required. Knowledge of scientific programming
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Mechanical Engineering or equivalent qualification The ideal candidate will have a master's degree in mechanical engineering, with a solid background in fluid dynamics, thermodynamics, and numerical methods
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skills to model and design optical systems for sustainable high-tech devices for billions of people? Do you like to develop and analyze numerical methods for partial differential equations? Information