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on experimental analysis, theoretical understanding and predictive modelling of complex mechanical behavior in engineering materials at different length scales (e.g, plasticity, damage, fracture), which emerges
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of interior spaces; and to develop a computer vision and deep/reinforcement learning approaches to combine and integrate imagery from the UAV, but also satellite imagery or data from other environmental sensors
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. Until now, specific EN fingerprints of localized corrosion are determined manually. This is a tedious procedure that requires considerable expert knowledge. Artificial intelligence or machine learning (AI
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make extensive use of low-fidelity simulations which can provide fast but inaccurate solutions depending on the flow complexity. To close this gap, this PhD will explore machine-learning (ML) methods
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develop a computer vision and deep/reinforcement learning approaches to combine and integrate imagery from the UAV, but also satellite imagery or data from other environmental sensors. Besides this, you
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, this PhD will explore machine-learning (ML) methods to significantly reduce the turnaround time of SRS, thus enabling their use for industrial design processes. By combining state-of-the-art numerical
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Kusterer), marketing strategy (Gerrit van Bruggen), deep learning (Sebastian Gabel), consumer and firm networks (Xi Chen), customer analytics (Aurélie Lemmens), and consumer learning (Maciej Szymanowski
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. Visit our LinkedIn page here. Job Requirements A Master’s degree in engineering, physics, applied mathematics, or a related field Experience and a deep understanding of one or more of the following
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, Antonia Krefeld-Schwalb, Anne Klesse, Bram Van den Bergh) in the domain of reinforcement learning, deep learning, causal inference, field experiments, consumer behavior, human-AI interactions, behaviorial
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Do you have a background in deep learning and computer vision? Are you