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The Medicines And Healthcare Products Regulatory Agency; | Canary Wharf, England | United Kingdom | about 1 month ago
machine learning where appropriate. The successful candidate will have: A PhD in Data Science, Statistics, Machine Learning, Computer Science or a similar quantitative discipline or demonstrable equivalent
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, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing
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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms
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for light–matter interaction in hyperuniform disordered plasmonic structures, including electromagnetic modelling, optimisation of metal–dielectric–metal resonators, and physics-informed machine-learning
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-holders will be expected to design and teach core and optional undergraduate and postgraduate modules, on the Undergraduate and Postgraduate levels, contributing to our outstanding teaching profile
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mechanical design, materials selection, and machining techniques. Key Responsibilities: Apparatus Construction & Modification: Fabricate and alter research and teaching apparatus from verbal instructions
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We are looking for a Research Assistant Reporting to Prof Yee-Whye Teh. The post holder will be a member of Oxford Computational Statistics and Machine Learning (OxCSML) with responsibility
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analytical backbone of the programme. It develops sensor-enabled diagnostic cells, multi-modal data pipelines and hybrid physics-informed machine learning approaches to understand interfacial behaviour during
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language processing, statistical machine learning and causal inference. The scholarship will fund course fees up to the value of home fees*, a tax-free stipend of no less than £20,780 per annum), plus additional
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into company valuations. You'll apply cutting-edge machine learning techniques (transformer models, causal forests, double machine learning) to understand which aspects of patent language predict valuable