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Field
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microbiology and bioinformatics approaches, we will consider appropriate expertise in either microbiology, molecular biology or bioinformatics with the desire to learn about the other aspects. The willingness
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systems safer, more efficient, and more sustainable. The aim of this project is to design a smart cognitive navigation framework that information from various sensors and learn to make decisions on its own
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is appropriately multi-disciplinary, at the interface between AI, environmental science, meteorology and epidemiology. Corresponding skills (machine learning, environmental and public health data
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. We value our doctoral researchers and aim to attract students from across the globe who are independent thinkers, restless to learn more and challenge themselves. We particularly welcome applicants
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by the University of Liverpool. As well as gaining a formal HE teaching qualification, you’ll learn key pedagogical skills for the academic job market, primarily through shadowing mentored classroom
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erosion and subsequent effect on land-to-lake dynamics using isotope tracer and source apportionment methodology at test sites in the Winam Gulf. (2) Explore use of remote sensing data and machine learning
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. when do we stop modelling? How do we track / score the quality of the model What is the required level of quality over time How can quality be brought to the required level Can Machine Learning, Large
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Matias, Adrián Villaseñor, Rowena Jacobs Topic 4-Machine Learning for Causal Inference in Health Policy Assessment for Decision Making Supervisors - Dr Julia Hatamyar and Professor Andrea Manca Topic 5
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training programme by the University of Liverpool. As well as gaining a formal HE teaching qualification, you’ll learn key pedagogical skills for the academic job market, primarily through shadowing mentored
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of reinforcement learning or agent-based systems. LanguagesENGLISHLevelExcellent Research FieldComputer science » Computer systemsYears of Research Experience1 - 4 Additional Information Benefits • Full funding