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Information - Personvernregler for databehandling Utløpsdato: ett år Navn: CookieInformationConsent Leverandør: www.jobbnorge.no Databehandlingsansvarlig: HAProxy software Formål: Kreves for at nettsiden skal
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experimental setups to predict Loss of Containment (LOC) in critical components of hazardous equipment (e.g. nozzles, elbows, flanges). Conduct axial/bending tests to identify leakage thresholds and formulate
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/Python) and/or structural analysis software (e.g., OpenSees, ABAQUS) is highly desirable Willingness to participate in experimental testing campaigns and multi-national secondments Familiarity with seismic
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, integrity-aware multi-domain navigation benchmark and associated algorithms, tested in realistic operational environments. The outputs will support standardisation efforts, accelerate cross-domain navigation
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. - Strong motivation for scientific research, ability to work independently and effectively plan and organize research tasks - Familiarity with standard office software and scientific data presentation
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tunnel testing and statistical modelling using extreme value theory, the project will yield predictive frameworks for structural risk under rare yet damaging wind conditions. The results will support
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algorithms for application parallelization, simulators and virtual platforms for application- and architecture exploration, hardware/software co-design and operating/runtime systems. Typical application
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 3 months ago
knowledge of software development and related tools (version control, compilation, documentation, testing, debugging…); Taste for technological innovation Fluency in English technical Good writing skills
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programming (MATLAB/Python) and/or structural analysis software (e.g., OpenSees, ABAQUS) is highly desirable Willingness to participate in experimental testing campaigns and multi-national secondments
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-training that facilitate few-shot adaptations. The research will show how the complementarity of event-triggered learning and meta-learning can drastically increase RL efficiency at test time. The co-design