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packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process adaptive and scalable. The research effort will be directed towards the selective recovery in
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-quality datasets that are structured and annotated for simulation and machine learning. Develop Digital Twins and simulation pipelines for virtual validation and predictive performance assessment. Implement
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, Philosophy, Educational Sciences, and Health Sciences. Through our bachelor’s and master’s degrees, Professional Learning & Development programmes, and interdisciplinary research themes – including Emerging
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the consortium and cooperation across work packages. In addition to doing research, you will be required to teach at the education programs of IViR, including thesis supervision for the Dutch-language
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. Geometric or variational approaches to partial differential equations. Differential, metric, or algebraic geometry. Invariant theory or symmetry-based methods. Geometric data analysis. Scientific machine
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substantial experience with machine learning techniques. You have experience with the PreFer data challenge. You have experience with working with Dutch register data. You have the ability to identify
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Postdoc in Application of eXtended Reality for Inclusive Automated Vehicle and Road User Interaction
, supportive environment that fosters learning and professional growth. Job requirements Hold a PhD degree in a relevant field (e.g., Transportation, Human-Computer Interaction, Computer Science, or a closely
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of the clinical context where academic research is performed and data are collected. Proficiency in data science, large multidimensional and combined analysis of different data sets, e.g. with machine learning
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information systems, human-data interaction, and machine learning, making it a natural home for advancing data interaction in hybrid care. This specific position is in close collaboration with Catharina
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packaging and e-waste, while Exergy will develop the digital-twin and machine-learning tools that make the process adaptive and scalable. The research effort will be directed towards the selective recovery in