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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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(PhD, which is completed within the last 3 years) in virology or immunology. • Previous lab experience working with respiratory viruses such as influenza virus. • Experience working with human clinical
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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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of machine learning and computational approaches to modeling human learning and language, normally acquired through attainment of a PhD in Computer Science or equivalent formal training in similar field
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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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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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field Experience leveraging artificial intelligence or machine learning in the development of battery electrolytes and catalyst materials Demonstrated expertise in lithium–sulfur battery materials and
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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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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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Computing Applications group in CSD has an immediate opening for a Postdoctoral Research Associate to design, develop, and deploy machine-learning and high-performance computing workflows, algorithms, and