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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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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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OBJECTIVE Working with a high degree of independence and under general direction, the Research Assistant¿4 will serve as the senior technical lead for a multidisciplinary program that engineers gene¿encoded
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and any students who may be assisting with the research. Deal with problems that may affect the achievement of research objectives and deadlines. Promote equality and values diversity acting as a role
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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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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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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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or a numerate discipline OR equivalent experience. Broad knowledge of probabilistic models, Bayesian inference and machine learning methods. Good knowledge of R, Python or both (links to project source
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Supervised Machine Learning and Reinforcement Learning. The objective is to significantly enhance battery performance and longevity. While conventional methods rely on either physics-based models or high-level
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of Oslo. Job description A fully funded PhD position is available on the development of spatiotemporal statistical modelling of climate-sensitive infectious diseases, with a particular emphasis on Bayesian