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to the above requirements • Strong background in optimization and partial differential equations • Strong background in numerical mathematics and computing • Machine learning skills are welcome • English skills
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optimizing simulation tools such as CalPhad to support experimental findings. Conducting in-depth metallographic analysis and establishing correlations between mechanical properties and microstructural
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multiplex and multilayer networks alongside with the observed links in order to predict or reconstruct the missing links. The first step is to explore different optimization methods using low rank tensor
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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neural networks under symmetry constraints, their optimization dynamics, and their generalization behavior—particularly in low-data or out-of-distribution settings. The work combines formal theoretical
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with neuroimaging, numerical mathematics, optimization, inverse problems, software development, motivation and research interests. The location for this research will be the workgroup of Prof. Dr
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-of-the-art sparse algorithm in matrices, tensor and networks for large-scale numerical, scientific and AI models and disseminating findings through publications and presentations in top-tier peer-reviewed
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to numerous preclinical research projects focused on the development of novel molecular magnetic resonance imaging (MRI)-based techniques for early detection, disease phenotyping and monitoring treatment
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testing, and advanced process simulation, with the objective of optimizing grinding performance and enhancing resource recovery. The ideal candidate will have a strong background in mineral processing
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scientists with numerous international collaborations and partnerships and have funding from the National Institutes of Health and the Canadian Institutes of Health Research. This position will join a diverse