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UiO/Anders Lien 1st August 2026 Languages English English English PhD Research Fellow-Continuous Experience Recognition for Distributed Co-creative Immersive Systems Apply for this job See
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Hydroinformatics – Uncertainty-Aware Optimisation for Robust Water Distribution System Operation The PhD candidate will develop uncertainty-aware, model-based multi-objective optimisation methods to support robust
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are seeking an exceptional candidate to join our dynamic team as a Principal Research Engineer or Research Fellow to lead the development of next generation technologies and strategies for distributed data
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Research Fellow-Continuous Experience Recognition for Distributed Co-creative Immersive Systems Apply for this job See advertisement About the position We invite applications for a PhD Research Fellow
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Management of Water Distribution Systems (WP3) + i3WaterS i3WaterS brings together leading universities, research centres, technology developers and water utilities across Europe. The project will train 15
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the spatial distribution of stomach bugs and antibiotic resistance are linked to other risk factors. It will use national data to: Map how infections and resistance change across time and regions. Explore how
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 3 months ago
to build a distributed collaborative file system where control over the data is given to users who can share it directly only with the users they trust and without having to store it at a central authority
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to deliver on an industry innovation research project where you will be part of the research team to design, develop and evaluate schemes for quantum key distribution networks and systems. Key
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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High
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hospital or population often fail when applied elsewhere due to distributional shifts. Since acquiring new labeled data is often costly or infeasible due to rare diseases, limited expert availability, and