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at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
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About us VIB.AI, the VIB Center for AI & Computational Biology, is a research center dedicated to integrating machine learning with deep biological insight to understand complex biological systems
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for therapeutic intervention. Initially focused on tools to study human protein complexes, the lab has expanded to novel screening platforms in human cells for applications such as Deep Mutational Scanning and
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accelerate breakthroughs in the understanding and treatment of neurodegenerative and neurological disorders. By combining deep mechanistic insight with translational ambition, the center aims to develop next
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expertise to dig deep into computational modelling, while working closely together with the experimental side of the lab. This interdisciplinary atmosphere has been a main catalyst for many past successes
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of novel mechanistic insights is gained through the application of novel probabilistic deep-learning models that automatically extract biological and statistical knowledge from your in vivo perturbational
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are looking for a team player who is open to learn about the newest technologies in single-cell and spatial transcriptomics and flow cytometry. Key Responsibilities Collaborate with PhD students, postdoctoral
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groups within CCB (Learn more: Bioinformatics & AI ). Your Role We are seeking a skilled computational biologist to organize and deliver training on bioinformatics and AI for the CCB research community