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Field
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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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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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, the impact of blowing snow on local and regional scales. The PhD candidate will produce mass balance simulations that support estimates of snow distribution for biodiversity and ecosystem assessments, as
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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
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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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the gravitational lensing effect caused by the distribution of dark matter in our Universe. The successful candidate will be trained to work on both galaxy clustering and weak gravitational lensing, using N-body
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Generation architectures, focusing on the integration of heterogeneous and distributed data sources. Prototypes will be implemented to evaluate different architectural choices based on an orchestrator
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linked EHR data. This project explores socioeconomic and ethnic inequalities in respiratory virus transmission and their impact on GP services in winter and whether these pressures are evenly distributed
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populations and biobanks for risk prediction, genetic discovery, and genomic medicine. Federated and transfer learning for distributed and privacy-preserving data integration. AI and Deep learning approaches