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, including deep learning architectures, self-supervised and unsupervised approaches, physics-informed neural networks, transformer-based models, and/or quantum-inspired learning techniques, capable
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leading scientists across Austria in an interdisciplinary environment spanning explainable AI, causality, knowledge representation, and neural networks. Research (90%) research in probabilistic machine
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2026 Interviews: TBC (online) Start date: September 2026 Project Title: AI-Enhanced Battery State of Health Estimation Using Ring Probabilistic Logic Neural Networks Director of Studies: Prof Shahab
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 18 days ago
(graph neural networks and transformers) The objective is to learn conformational heterogeneity directly from molecular dynamics simulations and to identify and predict allosteric communication pathways
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs
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. Experience in deep learning, computer vision, or neural network development. Experience with live-cell microscopy, fluorescence microscopy, or analysis of 3D/4D image data. Experience in cell biological
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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, and rigorously evaluate machine learning and deep learning models (CNNs, DNNs, transformers, graph neural networks, diffusion models, multimodal models, reinforcement learning) as well as software