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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
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develop and maintain the software for this shared demonstrator vehicle. Job requirements Completed (or about to complete) a MSc degree related to any of: artificial intelligence, machine learning
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to 1) Object-attribute compositionality to replace exhaustive data requirements with structured concept learning, 2) Bias detection and machine unlearning to identify and mitigate bias and shortcuts
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
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hybrid AI (combining machine learning, feature-based modelling, and classical OR); Designing intelligent release, workload control and material planning methods that stabilize flow, improve on-time
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from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques to uncover
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the response model from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques
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FPGAs, CGRAs, and many Machine Learning accelerators, offer significant opportunities for improving performance and energy efficiency compared to traditional CPUs/GPUs. Yet, porting and optimizing code
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preferably with data analysis and machine learning (e.g., Python, AI frameworks). You have strong analytical and problem-solving skills, with the ability to translate complex clinical processes into structured
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, neuroimaging and clinical psychiatry, with direct clinical impact. Your main activities are: analyzing and integrating multimodal MRI data for biotype identification; applying machine learning and advanced