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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation
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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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the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine
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. Akyildiz, “Deep kernel learning-based channel estimation in ultra-massive MIMO communications at 0.06-10 THz,” Proc. 2019 IEEE Globecom Workshops (GC Wkshps), 2019, pp. 1–6. [8] J. Tan and L. Dai, “Wideband
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, deep learning, and cognitive psychology and ergonomics. The EnACA project consortium includes the Computer Science, Image, and Interaction Laboratory (L3I/EA2118, University La Rochelle), the Fundamental
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., Tacchetti, A., Bakker, M.A. et al. Scaffolding Cooperation in Human Groups with Deep Reinforcement Learning. Nat Hum Behav 7, 1787–1796 (2023). [22] Melnyk I., Mroueh Y ., Belgodere B., Rigotti M., Nitsure A
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perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon these pNTA results
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information sources and to provide the relevant analysis of all the available variables in different scenarios conditions. In order to reach this goal, deep learning-based algorithms will be implemented