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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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topics: Development of deep learning models Mathematical foundations of machine learning Optimal deployment of AI systems The candidate is expected to hold a relevant MSc degree in Computer Science
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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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learning platforms Experience with deep learning, computer vision, medical image analysis or unsupervised learning is an advantage. English language skills, both written and spoken Qualification
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Bachelor’s degree in physics, computer science, computational biology, or similar fields. The applicant is expected to have demonstrated experience working with state-of-the-art deep learning approaches
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-motivated-language-model-security/ , and an Erasmus+ project on AI-based Personal Language Learning Assistants in collaboration with VSKDansk https://www.brondby.dk/om-kommunen/nyheder/2025/08/vskdansk-far
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and international peer-reviewed journals Experience and success with external funding (e.g. as co-applicant) Experience as programme lead as well as experience with and deep interest in the GLA:D
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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