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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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, allowing adapting to several conditions of use and to be managed automatically. AI algorithms will be selected, trained and tested. WP7 – Communication, dissemination and exploitation: Support for
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optimisation, algorithm design and high-performance computing, with application to airport innovation. Successful candidates will join an active group of Principal Investigators and researchers to work within
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learning paradigms. The framework will support rapid prototyping, automated design-space exploration, and cross-technology benchmarking, providing new insights into the co-design of learning algorithms
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processing algorithms Development and implementation of novel pulse sequences Acquisition of high quality NMR data Interaction with other research groups, learning new techniques where necessary Close
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and educational outcomes. Responsible AI and fairness auditing. Conduct algorithmic fairness validation of the CLARA system, develop documentation on data governance and GDPR compliance, and contribute
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: • Develop real-time optimization algorithms; • Model multi-vector energy-water-hydrogen systems; • Support the development of digital twins; • Test the algorithms using operational data; • Prepare a technical
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Job related to staff position within a Research Infrastructure? No Offer Description Functions: Collaborate on research tasks for the MIES project. Tasks: Develop data processing algorithms; draft
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techniques, or ontology alignment algorithms. - Solid grounding in software engineering practices: UML modelling, simulation, and testing under ISO/IEC/IEEE 29119. - Strong technical writing, documentation
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analysing large and complex databases to address research policy questions and improve analytics operations. Responsibilities To support the algorithm-driven allocation of research funds to higher education