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Field
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of artificial intelligence, machine learning and/or deep learning experience in scientific publishing and presenting research results knowledge or experience in public health research Personal skills Independence
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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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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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representing deep semantic and pedagogical structures in scientific and educational materials; high-fidelity extraction of conceptual and reasoning blocks; inference-time rationale generation; and adaptive
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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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, finite difference, and deep AI optimisation. You will receive training in all of these and various programming languages throughout the PhD. MetaHUB is a multi‑institution research collective, with
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optimization techniques. You have experience with modern Deep Learning Frameworks (PyTorch, Tensorflow, Jax) and proven ability of CUDA and Python programming. Knowledge of, or prior experience with, optimizing
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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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well as experience in omics data analysis, and possesses solid English-language skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX
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science/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning