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Number AE2025-0514 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0514.pdf CALL FOR GRANT
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modeling and numerical methods Experience with multiphase flow modeling (e.g., TFM, CFD-DEM, DNS, LBM) Solid programming skills Experience working in Linux/HPC environments Ability to conduct independent
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, applied mathematics, or a closely related field. Strong background in computational modeling and numerical methods Experience with multiphase flow modeling (e.g., TFM, CFD-DEM, DNS, LBM) Solid programming
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changes), Computer Science and Informatics (Numerical Analysis; simulation, optimization and modelling tools; Computational Fluid Dynamics (CFD)), Product and Processes Engineering (Space Engineering
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initiatives is highly desirable. Experience with computational tools (e.g., CFD, FEA, system-level modeling) and/or experimental platforms for energy systems is expected. Position # 2 - Machine Learning and AI
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will be the Head of Department. About the project The successful candidate will be part of the project SAPPHIRE (https://sapphire.norceresearch.no/ ). SAPPHIRE aims to contribute to cleaner and more
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experienced by hypersonic vehicles and quantifying the overall uncertainty. The candidate will assume the role of a software developer in the Computational Fluid Dynamics (CFD) and Propulsion Laboratory, a
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along with their simulation results (e.g. FEM, CFD) start to “pile up”, rarely being ever used after they have served their purpose. These models can be also seen in the context of product life cycle, i.e
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that are relevant to industry demands while working on research projects in SIT. The researcher will be part of the team of the MCCS NATURE Project (https://www.nparks.gov.sg/Cuge/Programmes-Schemes/Research
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and observables outside the training dataset. Task 4 - Integration of the trained TBNN model into the open-source CFD software, OpenFOAM. Perform a series of simulations to validate the trained TBNN