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, Create insightful graphs and other graphical tools to present outcomes back to farmers, Develop reporting for specific groups of farmers that are for instance in a specific supply chain or a learning
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | 27 days ago
Conference on Communication, Control, and Computing 2023. [9] F. De Moor, O. Boullé, D. Lavenier, De Bruijn Graph Partitioning for Scalable and Accurate DNA Storage Processing, BioRxiv, 2025. [10] M. C. Davey
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representations, and flow matching) for uncertainty-aware 3D reconstruction of coronary anatomy from 2D X-ray angiography; develop physics-informed neural networks and graph-based neural operators for fast
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of coronary anatomy from 2D X-ray angiography; develop physics-informed neural networks and graph-based neural operators for fast estimation of 3D coronary hemodynamics (velocity, pressure, and wall shear
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, and EHR data. Experience with modern deep learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, and SciPy. Familiarity with convolutional neural networks (CNNs), graph
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control with Git, typesetting with LaTeX, use of Linux computers; Experience with convolution and transformer-based neural networks for image analysis; Experience with graph-based methods, and graph
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al., “Deep Transfer Learning for Fault Diagnosis”, IEEE Transactions on Industrial Electronics, 2020. • Zhang C. et al., “Graph Neural Networks for Power Systems”, Electric Power Systems Research, 2023
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graphs and network medicine • Translational data science for therapeutic discovery Primary Responsibilities: The Postdoctoral Research Associate is expected to lead and contribute to independent and
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assurance, data science, data-driven modelling, digital manufacturing workflows, and Digital Product Passport B.3 Hands-on experience in machine learning, ontologies and knowledge graphs, IIoT and digital
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of continual graph learning. Continual graph learning studies how graph neural networks can learn from a sequence of evolving tasks, graphs, or distributions while retaining previously acquired knowledges