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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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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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advancements and practical implementations optimized for modern HPC systems. The postdoc will primarily contribute to one or more of the following research areas: Development of efficient numerical linear
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of the department's modern laboratory-, core- and animal facilities. The Department of Biomedicine focuses on innovation, entrepreneurship and collaboration with business and industry, and numerous researchers from
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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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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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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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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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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