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and Distributed Systems Research Group and the Robotics and Intelligent Systems Research Group. The research groups consist of around 30 full- and part-time faculty members and several postdoctoral
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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to candidates of all nationalities. Our colleague Philipp Assmy wants to hear from potential candidates interested in joining his team to determine the distribution, impact, and environmental drivers
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of images of galaxies with photometric redshifts that can be used to extract the gravitational lensing effect caused by the distribution of dark matter in our Universe. The successful candidate will be
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getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution over the parameter space) without specifying a model nor a prior. Such methods can in principle be applied
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mass balance simulations that support estimates of snow distribution for biodiversity and ecosystem assessments, as well as hydrological modelling and management plans for ski resorts and hydropower
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quantification, in particular the theory and methods known as predictive Bayes. Predictive Bayes theory involves getting Bayesian type uncertainty for parameters given data (i.e., a posterior type distribution
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leading. The faculty has approximately 3,000 students and around 500 employees, distributed across the faculty administration and six departments: the Department of Electrical Engineering and Computer
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algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process communication in parallel/distributed AI/ML
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at Blindern, Oslo. Job description The PhD fellowship position is located at the Department of Informatics and is hosted jointly by the Network and Distributed Systems Research Group and the Robotics