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estimation, calibration, and out-of-distribution detection. The PhD candidate will work on novel algorithms, theoretical insights, and large-scale empirical evaluations, with a strong emphasis on
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systems Experience in deep learning, computer vision, or multimodal data integration Exposure to federated learning, privacy preserving analytics, or distributed systems Knowledge of clinical data models
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- Quantum Reinforcement Learning, quantum computing, QKD - quantum key distribution, entanglement distribution System-level design and optimization AI & Intelligence: Agentic AI, Edge AI, information
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