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algorithms for inference and decision-making by pushing the boundaries of computational techniques. The research emphasizes efficiency in resource and data usage, reducing environmental impact, and ensuring
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close collaboration with industry and the public sector. The primary objective of SURE-AI is to create a new generation of algorithms for inference and decision-making by pushing the boundaries
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probability and statistical inference, optimisation, microeconomics, scientific methods Elective courses in economics, finance, or optimisation Dissertation on a topic in business economics, industrial
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the study of plant-plant or plant-invertebrate interactions Experience in nematology or nematological methods Strong quantitative skills (e.g., generalized linear mixed models, permutational methods, Bayesian
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mixed models, permutational methods, Bayesian analyses, machine learning algorithms, structural equation modeling). A good practical knowledge of R Personal characteristics To complete a doctoral degree
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qualifications Strong publication record in peer-review journals Experience with obtaining external funding Knowledge of and experience with marine biology and ecology Experience with demographic genomic inference
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analysis and coalescent based species inference, and synthesis in the form of a formal revision. Supervisors: Prof. Michael D. Pirie (main; Department of Natural History, University Museum, University
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processing for high-throughput sequencing, bioinformatics including phylogenetic analysis and coalescent based species inference, and synthesis in the form of a formal revision. Supervisors: Prof. Michael D
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epidemiological methods, causal inference and machine learning techniques, we aim to: Improve understanding of risk factors for primary headaches Predict diagnosis and disease progression Identify the most