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Field
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polymer synthesis, you will employ state-of-the-art characterization methods and test your materials in realistic fuel cell devices, in close collaboration with industrial partners. This position offers a
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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memristive devices, in collaboration with PGI-14. The goal is to train a system that leverages the intrinsic non-linear dynamics of these devices to perform complex learning tasks with extreme energy
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the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will strengthen the data science and machine learning activities of the IAS-9 with
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European project (IsoPROPEL) in close collaboration with Forschungszentrum Jülich (Germany) for cell design and catalytic testing and ITQ Valencia (Spain) for the synthesis of catalysts. The overall goal
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epidemiology, causal inference, genetic epidemiology, and machine learning. As a PhD candidate in the project, you will: Actively participate in group meetings, design statistical analysis plans in collaboration
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demonstrated ability to collaborate across multidisciplinary teams. The Postdoctoral Associate will help to transform and expand the way purpose is understood, accessed, and developed. About PSIX PSiX emerges
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work with large-scale behavioural data sets using a range of approaches, including heritability analyses and machine learning. Some data for the project already exist, but additional data will be
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
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through publications and presentations at leading conferences. This project will be undertaken in collaboration with Dr Feras Dayoub of the Australian Institute for Machine Learning, and Advanced Systems