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on addressing the complexity of the device-software-application-data design space, enabling systematic and efficient exploration using modeling and simulation tools.## Key Responsibilities- Identify and
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analytical sciences. We are looking for talented people to join us. Your responsibilities include: Interdisciplinary research within the project "Complex and Competing Phenomena in Recycled Flame-retardant
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new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health records
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range of industries face the challenge of managing increasingly complex stochastic systems, where uncertainty is inherent and data is abundant. These systems arise in diverse domains—such as manufacturing
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coordinating all data and research needs within the demonstrator sites especially in relation to ethics and general data protection regulation (GDPR). They will coordinate all work in relation to complex data
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data mining. The group provides a strong network to local AI expertise (e. g. Hessian.AI, TU Darmstadt), large scale compute infrastructure, as well as a broad international network (Stanford, UC San
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modelling, CFD mesh generation in complex 3D geometries. Proficient in handling large data sets and the ability to analysis and interpret results. Experience in commercial CFD package such as ANSYS package
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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methods (e.g., SGTCCA-NET) for the project's needs. Develop and implement novel statistical approaches for multi-omics network analysis. Update and improve existing R code to incorporate methodological
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and rheumatology. Dr Clayton also leads the cross-college Birmingham Translational Mitochondrial Research Network, providing additional opportunities for the candidate to expand their network and work