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on challenges related to computer vision for geophysical image data. You will investigate and develop robust methods for training models, exploring/developing approaches for multimodal data, utilize context
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with Emerging Interactive Technologies Apply for this job See advertisement About the position Position as PhD Research Fellow in Interaction Design / Human-Computer Interaction available
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-, Master's- and PhD degrees. If you have not yet completed your PhD, you must provide confirmation on your estimated date for the doctoral dissertation, or confirmation that your PhD thesis has been submitted
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on (ground-, soil-)water chemistry and carbon in cold climate zones (e.g. permafrost regions in Finnmark) and to expand hydrogeological observations during upcoming field campaigns. Potential methods include
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. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning models that forecast
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cooperation with the Institute of Marine Research. The core of the position will be on development of new deep learning methods for segmentation/classification of data with limited and weak labels. Your may
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at Department of Biosciences, University of Oslo (UiO), within the Convergence Environment ’Methods for knowledge-based biodiversity monitoring and management under uncertainty’ (BioM). Starting date: Between
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actually effective in Europe. To this end, the postdoc will use econometric methods, such as those that have been successfully applied to similar trade datasets. The postdoc will also examine to which extent
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PhD Research Fellow in statistical population ecology is available at Department of Biosciences, University of Oslo (UiO), within the Convergence Environment ’Methods for knowledge-based biodiversity
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qualifications: A strong background in algebraic topology. Experience in any of the following: Higher category theory, stable homotopy theory, simplicial methods. An interest in computer algebra and programming