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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow – Human Neuroimmunology, Brain Organoids and Multi-omics Department MS Research Network | Department of Medicine
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world models with generative AI and theory-informed neural networks. Job Responsibilities: Conducting theoretical and empirical studies on economic world models, with a strong emphasis on critical finance
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apply machine learning and deep learning models (e.g., graph neural networks, generative models, transfer learning) for materials property prediction, interpretation, and inverse design. Perform high
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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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on Artificial Neural Networks and Gaussian Process modelling, to accelerate processing optimisation. Consolidate experimental, techno‑economic, and sustainability data into robust technical evidence packages
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-Informed Neural Networks (PINNs) and hybrid models that respect the physical laws governing the real-world system Applying Deep Reinforcement Learning (DRL) algorithms to optimize processes within simulation
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, Mathematics or Computer Science (or a related field) with an emphasis on applied mathematics/statistics/machine-learning. Prior experience in the probabilistic machine-learning, neural-networks, statistical
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, neural networks) to be able to analyze data sources and bias mechanisms. Knowledge of the challenges of interoperability and digital infrastructure in resource-constrained countries. Knowledge of African
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of multi-agent coordination, decentralized control, target assignment, or swarm robotics. Familiarity with graph neural networks, attention mechanisms would be advantageous. Experience with computer vision
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, Mathematics or Computer Science (or a related field) with an emphasis on applied mathematics/statistics/machine-learning. Prior experience in the probabilistic machine-learning, neural-networks, statistical