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assessing and interpreting uncertainty in deep learning models. As a person, you are curious, self-driven, and enjoy working collaboratively in multidisciplinary research environments. Awareness of diversity
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plants). The associate will develop rigorous, scalable methods—spanning physics-based and data-driven models—for optimal power flow, demand response of flexible loads, transient/stability-aware dispatch
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highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud
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for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in
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medicine, with a primary focus on optimizing clinical trial design. The partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry
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medicine, with a primary focus on optimizing clinical trial design. The partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry
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strong background in machine learning, computer vision, or data-driven modeling. You have extensive experience in the development and implementation of AI and machine learning algorithms, ideally with
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. The topic of this Postdoc position is the development of data-driven methods to estimate river streamflowusingsatellitealtimetry. Particularly, the Postdoc willexplore hybrid deep learning models
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programmes in wind farm and power transmission system model, analysis, control and optimisation but not limited to data driven monitoring for control and operation. What we are looking for: You must have a
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular