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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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, or representation learning. Experience analysing large-scale single-cell omics data. Experience with integrative multi-omics data, such as genomics, proteomics, or metabolomics. Experience with relevant machine
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.) You will work with one of the most comprehensive multimodal datasets available, enabling research at the frontier of data-driven biology. What you’ll do Develop and train large-scale machine learning
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one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models. Rules governing PhD students are set
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data with computational modeling Programming skills in Python, R, or another relevant language. Interest in machine learning, statistical modeling, structural bioinformatics, or analysis of large-scale
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, proteomics, long-read sequencing). Familiarity with machine learning approaches, particularly artificial neural networks, and their application to biological data. Experience with workflow management systems