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Bayesian framework and two specific proposed lines of research: (1) constructing suitable priors via neural networks approximations, and (2) enhancing the sensitivity and efficiency of posterior diagnostics
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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: detection of objects and relations between objects, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and
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partner or with another humanoid robot to solve a spatial problem (e.g. 3D puzzle, fold a paper). The tasks to be carried out are: (i) scene understanding: detection of objects and relations between objects
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allows for objective and qualitative assessments. A complete application should be submitted by the application deadline. An incomplete application may jeopardise a fair assessment of qualifications
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. The aim to advance the use of X-ray techniques through a project focusing on X-ray tomography, opening up for 3D characterization of materials developed in FibRe. In particular, the objective is to
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to meet the project’s objectives. Collaboration is key—you will work closely with researchers and PhD students within the Division of Marine Technology. Your responsibilities Your main responsibilities will
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for autonomous shipping in restricted waterways. A database of model examples has already been developed, and your work will build upon and refine these models to meet the project’s objectives. Collaboration is
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the use of X-ray techniques through a project focusing on X-ray tomography, opening up for 3D characterization of materials developed in FibRe. In particular, the objective is to through X-ray imaging study
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molecular structures capable of transferring electrons and interacting with light. Such assemblies also have applications in biomedicine. The primary objective is to develop computational methods, using deep