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evidence. · Investigating new ways to robustly and reliably evaluate domain-specific IR and RAG approaches to ensure that methods align with user expectations and preferences. · Collaborating with
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alternative energy to better food safety to breakthrough medical and environmental applications achieved through rare isotope research.We teach. We explore and we discover. We collaborate and lead. We innovate
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costs and energy requirements of state-of-the-art deep learning models significantly, while democratizing them for a vast community of users, researchers, and practitioners. The task is to perform just
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through rare isotope research.We teach. We explore and we discover. We collaborate and lead. We innovate, inspire, and empower. We achieve our potential and create circumstances that help our students and
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users, thanks to the use of machine learning tools and techno-economic analyses. This project is aligned with the sustainable development goals (SDG) 7 and 10 of the United Nations, by promoting a low
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English. Preference will be given to those with (i) strong background in quantitative methods, geospatial methods, AI and machine learning; (ii) experience in high-performance and cloud computing; (iii
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for robot intelligence, with applications that naturally extend to marine and maritime domains. You will work closely with colleagues across the department and engage in strong collaborations with academic
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establish structure–property relationships Contribute to publications, reports, and project dissemination Collaborate with project partners in a defence and space R&D framework Is Your profile described below
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-FNR PEARL Research Grant in the area of Information Systems Engineering, and, depending on interest, in fields, such as Generative AI & Machine Learning, Data Privacy, Cyber Security, Digital Identities
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analytics Exploring and implementing federated learning and privacy-preserving AI approaches for distributed clinical datasets Collaborating closely with data providers, clinicians, and technical teams