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“Quantifying Efficacy and risks of solar radiation management (SRM) approaches using natural analogues”. The project will use novel machine learning-based methods to determine the climate response to a range of
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effects while building machine-learning-ready kinetic datasets for predictive catalyst design. You should have a PhD (or about to obtain) in Chemistry or field related to this project (Chemical Engineering
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the other group members, and publish them in high-impact journals. In addition to research, you will also be expected to take part in the supervision of PhD students and the teaching activities
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sophisticated machine learning tools for image processing Experience in mathematical modelling Knowledge in comparative neuroscience (comparative vertebrate neuro) Proficiency in basic computer packages (eg
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reports and grant proposals. You should possess a PhD or DPhil (or near completion of) in Machine Learning or Maths. Informal enquiries may be addressed to jakob@robots.ox.ac.uk For more information about
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developing cutting-edge active-learning (Bayesian optimisation) methods that integrate chemical knowledge by capitalising on Large Language Models (LLMs) as well as human knowledge. You should have a PhD in
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Are you passionate about using data science and machine learning to address mental health inequalities in rural and coastal communities? The University of Lincoln is seeking an ambitious
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. About You You will have, or be close to completion of a PhD/DPhil in Statistics, Machine Learning, Data Science, or a related quantitative discipline. You will demonstrate strong specialist knowledge in
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close to completion of, a relevant PhD/DPhil in one of the following subjects: quantitative, genetic or molecular epidemiology, medical statistics or statistical genetics. You must have strong quantitative
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initiatives. To be considered, you must hold, or be close to completion of, a relevant PhD/DPhil in one of the following subjects: quantitative, genetic or molecular epidemiology, medical statistics