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Profile The ideal candidate should have: • Knowledge of machine learning, especially neural networks, graph neural networks, or federated learning. • Strong mathematical, optimization, and algorithmic
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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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-aware AI under practical deployment constraints. Familiarity with efficient neural network architectures, including alternative attention mechanisms or mixture-of-experts models. Exposure to trustworthy
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neural network has reduced this cost by a factor of 5000 (Radureau, 2025). This project aims to develop a mesoscopic radiative model to overcome the CFL constraint by adapting the average photon speed
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interdisciplinary environment spanning explainable AI, causality, knowledge representation, and neural networks. Research (90%) research in probabilistic machine learning and neuro-symbolic AI (e.g. neural nets
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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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techniques—particularly convolutional neural networks (CNNs)—will be applied to identify complex canopy patterns and classify successional stages. Mandatory requirements: Ph.D. in Remote Sensing, Forest
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. Indeed, the methods currently used rely on optical image databases of various avalanche observations. A deep neural network was trained on this data to enable automatic avalanche detection FIGURE 1 (a) [1
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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