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to: Conduct cutting edge research in machine learning, AI and algorithms, such as but not limited to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig
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-GRASP”, “Simulation-Based Bayesian Inference for Object Perception in Robot Grasping”, financed by the European Union´s Horizon Europe research & innovation programme under the euROBIN project (Grant
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 9 days ago
to Bayesian machine learning, human-centered AI and interpretable machine learning, attention markets, gig economies and prediction markets. Opportunity to supervise research students and work as part of a
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, large-grant project on the epidemiology of bovine tuberculosis in wild badgers, using state-of-the-art Bayesian modelling approaches to study the drivers of infectiousness and transmission of infection in
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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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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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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
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of current issues and future directions within the field of Active Inference, control theory or Bayesian inference. B7 Experience with building computational models of human users in an interaction setting. B8
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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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computational modeling, geometric morphometrics, multivariate and Bayesian statistics, spatiotemporal and spatial modeling (including GIS), causal inference, machine learning, AI, and statistical software