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RNA-Seq, ChIP/DAP-Seq protein-DNA interaction data, bulk, and single-cell ATAC-Seq) and the application of diverse supervised machine learning approaches (e.g., feature-based, deep learning, and
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are currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning. In particular, we develop machine learning methods to derive
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Description The Yves Moreau and Stein Aerts research labs are looking for a shared PhD student to apply deep representation learning and large language models to decipher the genomic regulatory code
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trajectories. Leveraging deep learning, generative models and cross-modal autoencoders and working with both structured and unstructured data, the goal of this project is to develop a statistical framework and a
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, Molecular Biology, Natural Sciences or related disciplines be very motivated and enthusiastic to learn and expand both computational and experimental skill sets have an analytical mindset be able to summarize
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both experimental and computational tasks. While prior experience in computational analysis is not a requirement, a strong willingness to learn it is essential. Support from fellow lab members is
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should: have an MSc in Systems Biology, Molecular Biology, Natural Sciences, or related disciplines be very motivated and enthusiastic to learn and expand both computational and experimental skill sets be