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to facilitate the accomplishment of biodiversity conservation research objectives. Develops and writes new proposals to secure contracts for grant-funded research related to biodiversity conservation and the use
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for: Operational research and combinatorial optimization (e.g., solvers Gurobi, CPLEX, Hexaly) Bayesian optimization, evolutionary algorithms, or hybrid methods Multi-objective and constrained optimization Surrogate
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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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research objectives and proposals for own or joint research including research funding proposals To attend and or present at conferences/seminars at a local and national level as required To undertake
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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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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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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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Research Associate to contribute to a project focused on robust Bayesian inference with possibility theory. Robust inference is crucial for many real applications in which datasets are invariably corrupted
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the team’s work across its different content areas. We are seeking a candidate with strong quantitative and statistical modeling skills, particularly in Bayesian methods, who is ready to advance their career
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of rail with wider city and regional transport networks. A focus of this work is the application of optimisation techniques (e.g. evolutionary algorithms, or Bayesian techniques) to identify high performing