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. Examples of research topics include but are not limited to: development of new AI architectures for biology and hybrid models that combine deep learning with mechanistic models; foundation models
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understand how genetic variation shapes biological function. At the heart of our research is MAGESTIC ( https://doi.org/10.1038/nbt.4137 ), a CRISPR-based technology that enables precise engineering of
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data. Manage preferences for further information and to change your choices. Accept all cookies Reject optional cookies Skip to main content Machine Learning (ML) Engineer at VIB.AI Employer VIB
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technologies to understand how genetic variation shapes biological function. At the heart of our research is MAGESTIC ( https://doi.org/10.1038/nbt.4137 ), a CRISPR-based technology that enables precise
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based modeling of protein structure and protein interaction
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architectures for biology and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based modeling
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based modeling of protein structure and
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based modeling of protein structure and
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directly contributes to protecting world-class scientific research. A collaborative ICT team where knowledge sharing and continuous learning are strongly encouraged. Opportunities for professional growth
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to: development of new AI architectures for biology and hybrid models that combine deep learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi