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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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this project, we will focus on increasing the computational efficiency of interacting particle methods for Bayesian inversion when including model error in a multilevel hierarchy. As model problem, we will
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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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The Faculteit Geneeskunde en Farmacie, Department Observerende Klinische wetenschappen, Research Group Electronics and Informatics: Research – Development - Innovation is looking for a PhD-student
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should have a strong interest in computational mechanics, finite element modelling in particular, as well as in textile materials and should be enthusiastic to work in a collaborative project between
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borders? Requirements Master’s degree in Chemical Engineering, Process Engineering, Computer Science, or related fields. Strong background in modelling, simulation, and/or machine learning. Experience with
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The Faculteit Geneeskunde en Farmacie, Department Observerende Klinische wetenschappen, Research Group Electronics and Informatics: Research – Development - Innovation is looking for a PhD-student
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immunology (e.g. in vivo models, flow cytometry, imaging) Motivation to learn and apply computational biology/bioinformatics approaches (training will be provided). Some background in programming is therefore
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dynamics resulting from varying compositions. An adaptive Model Predictive Control strategy is envisaged for this challenge. During the four-year doctoral programme, you will contribute to the group's R&D
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block within this process. You will be embedded both within an experimental and computational team, providing a unique atmosphere where there is expertise to develop the deep-learning models while having