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and competitions. The University of Bristol seeks an aspiring researcher with an engineering, physical sciences or mathematical background and an aptitude for practical implementation of cutting edge
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. They will participate in the design and analysis of numerical experiments that will help explore the sensitivity of the model when input parameters (chlorophyll-a and -b content, leaf area index and
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reuse of energy systems analysis processes. In line with FAIR and Linked Open Data principles, you will design interfaces that enable the smooth processing of big data in the context of scientific, AI
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analyses, mathematical modelling, or data analysis. But whatever the research approach, the goal would be to influence UK policy that increases protection of the UK population from current or future emerging
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science with demonstrated experience in quantitative analysis. We are looking for a range of talents and expertise, including in machine learning, mathematics, or statistics. Experience with one of the focus
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(with a strong mathematical focus) with above average grades Advanced knowledge in probability; expertise in random graphs, complex networks, hyperbolic geometry or topological data analysis is a plus
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, Skills and Experience • You will have substantial technical experience in time series analysis, ideally either in neurophysiology data or wearable sensor data • You will have experience of at least
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, optimization techniques, and climate change scenario analysis. The successful candidate will contribute to the development of intelligent models and decision-support tools that enhance the performance
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. Responsibilities and qualifications The nature of this project suggests that you should have a strong interest in the mathematical and theoretical aspects of machine learning. A solid background in mathematics (e.g
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benefit from a co-supervisor based at the nearby John Innes Centre, a world-renowned centre for plant science, where you will spend time in the Biomolecular Analysis Facility. You will also have the