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, structure preserving deep learning, stochastic differential equations, generative AI, numerical optimization. Strong programming skills (Python, Julia, Jax). Experience with numerical optimization is also
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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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features, the PhD will first learn an individualised cognitive simulator that models how a person generates expressive responses under emotional, social or conversational contexts. The simulated cognition
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experience in Python and, more specifically, in common deep learning frameworks such as PyTorch and jax, for model training and inference have experience with embedded platforms such as FPGAs or RISC-V
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be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably
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, Mathematics, Engineering or a related discipline, with an interest in AI applied to complex and safety-critical systems. Good knowledge of Machine Learning and Deep Learning methods, including experience with
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centres, we provide an unparalleled learning environment for its 24,000 students and 13,000 staff. At Cambridge, our mission is to contribute to society through world-class education, learning, and research
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
) Collaborative Context and Expertise Since 2018, EDF has been studying the construction of meta-models based on deep neural networks (i.e., deep learning). Initially, based on fluid simulations using Code_Saturne
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an unnecessary discarding of generated power. This four year’s PhD position is aimed at using captured CO2 from point sources and hydrogen from water electrolysis to generate carbon-based base chemicals and fuels
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and economic use of renewable electricity without an unnecessary discarding of generated power. This four year’s PhD position is aimed at developing multiphase models to predict the dynamics and