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with large datasets, with a minimum 200 records, using statistical software packages including SAS, R, SPSS, and STATA. (Required) Demonstrated knowledge of at minimum one general object-oriented
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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of economically valuable species and invasive species that may compete with economically valuable species or promote wildfire. Learning Objectives: The postdoctoral fellow will have the opportunity to apply
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risk development. These projections will enable the identification of sustainable adaptation strategies and support policy development. Objectives Develop an Agent Based Model (ABM) that describes flood
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and supervision of postgraduate research students. To develop research objectives and proposals for own or joint research including research funding proposals To attend and or present at conferences
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the research. Deal with problems that may affect the achievement of research objectives and deadlines. Promote equality and values diversity acting as a role model and fostering an inclusive working culture
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integrate sophisticated AI systems, rigorously testing, validating, and tracking learning models, and troubleshooting issues to ensure system accuracy and reliability. A core objective of this role is to
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modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian hierarchical modeling using Integrated Nested Laplace Approximation (INLA). The work will contribute to ongoing
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techniques. The incumbent is expected to exercise sound judgment in selecting and applying appropriate methods and techniques to achieve research objectives, working independently within broadly defined
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research and soft robotics development. PhD project The PhD project will focus on the technical aspects of simulating the physics of the Drosophila larva body. The primary objectives include: Developing a