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Hospital. You will contribute to the development of innovative AI approaches for mental health research by designing neural networks and large language models for difficult-to-treat depression. You will
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optimally combined to deliver models with extremely constrained compute and memory footprints without compromising performance. This includes training spiking neural networks with multiple plasticities
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Grant Regulations of the Foundation for Science and Technology. 6. Work plan: Development of artificial intelligence-based models with physics-based neural networks (PINN) for structural mechanics. 7
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Multimodal Models Generative AI, Agentic AI, Physical AI, and Embodied AI Trustworthy AI, including explainability, auditability, and privacy Edge AI and model optimisation Physics-informed neural networks
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implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic pathway prediction. Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions
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. The postdoctoral fellow will lead the development of cutting-edge, explainable graph neural network (GNN) models that integrate microbiome functional profiles, host genetic variation, and behavioral phenotypes from
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. The ideal candidate should have a strong background in artificial intelligence and machine learning, with demonstrated experience in developing and training neural networks for predictive modeling. Position
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, - Computer Vision, - Autonomous Vehicles, - Robotics, - Intelligent Control, - Reinforcement Learning, - Physics-informed Neural Networks, - AI for Health and Biomedical Applications, - AI for Social Sciences
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future colliders. https://doi.org/10.1051/epjconf/201919901022 - Central exclusive production at LHCb https://arxiv.org/pdf/2507.13447 - Theory-Informed Neural Networks for Particle Physics Knowledge
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on developing hybrid traffic flow models that combine physical modelling principles with machine learning approaches, such as Physics-Informed Neural Networks (PINNs) and machine-learning-enhanced traffic models