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are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in mathematical, physical or computational sciences Experience in using machine learning methods
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will have or be close to completion of a PhD/DPhil in Health Economics or related quantitative discipline, OR have a Master’s degree in Health Economics* or related quantitative discipline along with
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to other members of the group on health economic methodologies or procedures. About You You will have or be close to completion of a PhD/DPhil in Health Economics or related quantitative discipline, OR have
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knowledge with Master’s and PhD students. You will actively contribute your expertise to the acquisition of third-party funding and independently advance the "AAAging" project. The independent teaching of
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for improving the environmental impacts of wood production over broad spatial scales. Required Skills and Qualifications: PhD in a forestry- or conservation-related discipline. Strong background in field survey
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Agency (ARIA). The PROTECT project (Probabilistic Forecasting of Climate Tipping Points) brings together cutting-edge AI, statistical, and machine learning techniques with climate modelling, aiming
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electronic health records (EHRs) from multiple UK hospital centres using advanced data analytics including machine learning, deep learning, and statistical techniques—with a particular emphasis on deep
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challenging analyses, overcoming statistical problems, producing high-quality visualisations and outputs, and delivering reproducible analysis scripts. You also need a strong track record of delivering high
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scholars in Law and two PhD students (one in Law and one in Computer Science/Data Analytics), as well as with international, European and national stakeholders involved in the CURE project. The post-holder
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of mole activity and soil health and biodiversity, collecting data on visitor perceptions of moles and their management, and analysing findings using statistical modelling approaches. The role provides