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algorithmic and theoretical foundations of reinforcement learning applied to complex, high-dimensional dynamical systems. The project focuses on the intersection of deep reinforcement learning
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Track Record: At least two first-author research papers at flagship venues of core machine learning research (e.g., NeurIPS, ICML, ICLR, AISTATS). Theoretical Rigor: A deep understanding of
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Biotech Research and Innovation Centre, The Lund Group, University of Copenhagen | Denmark | about 1 month ago
project is conducted in collaboration with the deep probabilistic programming group of Thomas Hamelryck: https://di.ku.dk/english/research/groups/machine-learning-in-biology/?pure=en/persons/271052
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, adapt and use deep learning methods for analyses of omics-based data obtained in the project. Collaborate with international partners and contribute to joint research efforts within the program
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emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods for analyses of omics-based data obtained in the project. Collaborate