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: (1) automated reconstruction of a visual and geometrical 4D Digital Twin based on visual computing; (2) usage of information from digital imaging techniques for estimation and prediction of current and
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open to candidates with a strong interest in either: i) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane
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) Radio/physical-layer intelligence (e.g., channel estimation, CSI prediction, edge-deployable deep learning), or ii) Networking and control-plane intelligence (e.g., reinforcement learning for scheduling
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to shape disease risk. Yet most clinical risk models ignore this exposome. In BEE, we will build explainable, physics-guided, GeoAI-driven models that: Predict acute and chronic NCD risks at the population
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models ignore this exposome. In BEE, we will build explainable, physics-guided, GeoAI-driven models that: Predict acute and chronic NCD risks at the population scale Identify vulnerable neighbourhoods and
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28 Feb 2026 Job Information Organisation/Company KU LEUVEN Department ORSTAT/FEB Research Field Computer science » Database management Computer science » Modelling tools Economics » Business