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, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting applications in forecasting
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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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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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unique D-MIMO testbed at Lund University, extending existing and creating new deep learning-based models for anomaly detection, theoretical and numerical studies of detection quality, creating new
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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transformations and 3D registration problems Familiarity with graph-based methods or geometric deep learning Interest in musculoskeletal biomechanics or translational medical technologies The PhD candidate will be
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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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, 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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existing and creating new deep learning-based models for anomaly detection, theoretical and numerical studies of detection quality, creating new distributed computational pipelines and optimizing
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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