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Uncertainty quantification for machine learning models of chemical reactivity In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions
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Fixed-term: The funds for this post are available for 48 months. The Project Applications are invited for a PhD student to work on machine-learning guided and verifiably correct code generation
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View All Vacancies Chemistry Location: UK Other Closing Date: Sunday 12 May 2024 Reference: SCI266 Uncertainty quantification for machine learning models of chemical reactivity In this PhD
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Computing Science is now looking for a Doctoral student in machine learning for continuous
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to staff position within a Research Infrastructure? No Offer Description Are you eager to apply cutting-edge machine learning techniques, develop innovative algorithms, and tackle real-life challenges
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The project you will work on lies on the boundary between AI and theoretical physics. Physics has been a source of inspiration for innovation since the early days of machine learning. In particular
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Architecture GroupO'Reilly InstituteDublin 2 STATUS: EXPIRED
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nonlinear interactions at the origin of such extreme events. In this project, we will explore the use of cutting-edge scientific machine learning framework that blends deep learning with physics-based
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will explore the use of cutting-edge scientific machine learning framework that blends deep learning with physics-based techniques to achieve the goals of (i) identifying precursors and mechanisms
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Project title: Using machine learning to evaluate atomic force microscopy nanoindentation data Supervisory Team: Dr Martin Stolz, Dr Sasan Mahmoodi Project description: The University of Southampton