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. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell, regulatory, or multimodal biological data. Support target and mechanism prioritization by integrating
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processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell
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genomic factors associated with increasing age, and vulnerability for neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease and others. For a deep dive into the effects of aging
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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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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 biomolecule structures
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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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architectures for (bio)medical research 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
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demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning
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‑on experience with common machine learning / deep learning frameworks (eg. PyTorch or JAX) applied to biological or structural data. Solid Python programming skills, with experience building maintainable and
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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