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hydrodynamic and CFD simulations. Coordinate procurement and liaison with vendors/suppliers. Work independently, as well as within a team, to ensure proper operation and maintenance of equipment. Job
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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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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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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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unrealistic for simulating a pilot-scale or industrial mixer with real powders [1]. Therefore, more macroscopic models need to be developed. CFD-type simulation methods [2] might be developed to that purpose
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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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designing against diffuse pollution. Typically, these processes are studied by laboratory experiments and/or computational fluid dynamics (CFD) approaches, often limited to an idealised patch of stems where a