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technology or system of higher education, in Portugal or abroad. 6.2 — The score obtained in the curricular evaluation method is expressed on a numeric scale of 0 to 20, considering the valuation up to two
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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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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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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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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This research project focuses on the study of advanced numerical methods
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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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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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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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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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and ability to work both independently and collaboratively Experience with deep learning frameworks, such as Tensorflow or Pytorch is advantageous Experience in numerical methods for partial