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                Employer- University of Oslo
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                for this job See advertisement This is NTNU NTNU is a broad-based university with a technical-scientific profile and a focus in professional education. The university is located in three cities with headquarters 
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                distributed data processing approaches to address issues of incomplete or redundant multimodal data, dynamic updates, and scalable semantic interoperability in large-scale DPP systems. Particular emphasis will 
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                directly into WP3 of DynAMIC, led by NERSC, to investigate pan-Arctic patterns in the sea ice deformation field and LKFs, with a newly-incorporated ridging scheme in the state-of-the-art neXtSIM: next 
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                analysis, that is medical images that evolve over time, with emphasis on dynamic PET imagery and ultrasound. A special focus will be given to learning from limited labeled data (e.g. few-shot and self 
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                and dynamics of the Late Weichselian Svalbard-Barents Sea Ice Sheet (SBSIS) is relatively well understood in comparison to the SBSIS during Marine Isotope Stage (MIS) 4 (c. 71-57 kyr BP) and the 
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                separation and wetting on cellular membranes Apply for this job See advertisement About the position A Postdoctoral Research Fellowship is available at the Mechanics Division, Department of Mathematics 
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                is at UiT in Tromsø. You must be able to start in the position within a reasonable time after receiving the offer. The position’s field of research The configuration and dynamics of the Late 
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                and numerical methods for analyzing stochastic transport equations, which are critical for understanding how random fluctuations affect dynamic systems such as turbulence, water waves, and compressible 
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                tourism, which in turn affect natural resources, cultural environments, and social development, creating complex, dynamic, interrelated systems that are constantly evolving. Stakeholders in local governance 
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                analysis, that is medical images that evolve over time, with emphasis on dynamic PET imagery and ultrasound. A special focus will be given to learning from limited labeled data (e.g. few-shot and self