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project between the Artificial Intelligence Unit and the Nuclear Technologies Unit, looking for talented and motivated candidates to start a new research line on applying AI techniques to model, simulate
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-layer flows—these approaches can recover unmeasured near-wall structures, improve subgrid-scale modelling, and enhance predictive accuracy. Possible project directions include: 1. Reconstructing near-wall
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cover both foundation-model pretraining and downstream predictive and generative applications. Your main responsibilities are to: design, implement and benchmark foundation-model architectures for
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Università degli Studi di Roma Tor Vergata - Dipartimento di Biomedicina e Prevenzione | Roma, Lazio | Italy | about 2 months ago
, an interpretable predictive model combining genomics, clonal dynamics (ΔVAF), CNV burden, and clinical variables, validated with time‑blocked cross‑validation, calibration, and decision‑curve analysis
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healthcare, The key objective is to spearheadresearch and development to revolutionise patient care and inform public health policies. For more details, please view https://www.ntu.edu.sg/c-aim We are seeking
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clinically relevant predictive models. The lab also maintains extensive collaborations with experimental and computational scientists across multiple academic, industry, and government partners. Trainees
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visibility. A prestigious three-year MSCA Fellowship. A competitive salary including mobility and family allowances. DC09: Improvement of IVRT and IVPT test procedures to make them more predictive of
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position that we support the transition across an immensely broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade
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ultimately shifts in species' distributions. This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species
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innovation project with a Swiss industrial partner. The focus is on sensor-based and model-based methods for real-time condition assessment and predictive maintenance of safety-critical mechanical