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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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concepts into measurable, reusable methods. Context: You will contribute to the ERC Starting Grant GAMECHAR (Scalable AI-Driven Framework for Gameplay Characterization; https://cordis.europa.eu/project/id
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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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and scalable. Design and build a technology demonstrator prototype of clinical-testing grade. Collaborate with interdisciplinary teams, including clinicians, engineers, and machine learning (ML) and
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limited. To learn from past warmer climates and better understand the link between climate and extremes, we can use proxy-based climate reconstructions and climate models for past warmer climates. However
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group Membrane Materials and Processes (MM/P – https://www.tue.nl/en/research/research-groups/membrane-materials-and-processes ), part of the Department of Chemical Engineering and Chemistry at the TU/e
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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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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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of writing; You have an interest in / affinity with colonial history and languages; You have experience with or are willing to learn about early modern archival sources; You have full professional working
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the Computer Engineering group. Curious to learn more about the project, and perhaps our group? Feel free to browse our webpages: About our department: QCE department . About our group: Computer Engineering Lab