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from animal studies to humans) in drug discovery, dynamical systems for long-horizon time series forecasting, and verifiably safe reinforcement learning. While both PhD positions are part of the same
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around us evolve over both time and space, making spatio-temporal processes and data omnipresent in science and technology, with applications ranging from weather forecasting to cardiovascular medicine
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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