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Field
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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
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
for representing knowledge graphs and OWL for formalising ontologies. In order to manage the heterogeneity of knowledge, alignments between ontologies make it possible to express the relationships between concepts
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thesis in the field 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