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Field
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models
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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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, - 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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. 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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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