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validation of the numerical model. Candidates should be holding a MSc degree in Engineering (or equivalent), with demonstrated experience in computational fluid dynamics (CFD), preferably in hydraulic
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inverse problems. The team aims at developing Bayesian computational methods for such (ill-posed) inverse problems and aims both at increasing their validity and at reducing their computational cost. In
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, Coastal Engineering, Mechanical Engineering, Hydraulics, Geosciences, or a related field Strong background in hydrodynamics, sediment transport, and numerical modeling Experience with computational tools
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-time Offer Starting Date 3 Feb 2026 Is the job funded through the EU Research Framework Programme? Horizon Europe Reference Number BAP-2025-694 Is the Job related to staff position within a Research
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, boosted by AI-data augmentation for extrapolating spectrum patterns from multiple sources. To design a scalable computing framework using a physics-informed neural network for distributed spectrum analysis
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. the light curves and spectra) of these stars analytically and through numerical methods, based on binary stellar evolution models. You will also investigate potential observable signatures of binary evolution
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the necessary theoretical support. Practical implementation of the developed methods is part of the project, and hence skills in computations and implementation (via codes written in open source software, such as
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Offer Starting Date 9 Mar 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number BAP-2025-697 Is the Job related to staff position within a
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- 23:59 (UTC) Type of Contract To be defined Job Status Full-time Offer Starting Date 12 Jan 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference
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Offer Starting Date 1 Apr 2026 Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Reference Number BAP-2025-593 Marie Curie Grant Agreement Number 101227458 Is the Job