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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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problems. Provide expertise in modern ML methods, including deep learning, foundation models, multimodal data integration, generative AI, and simulation-based inference. Engage with VIB’s AI Studio
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-human discovery engine platform, deep understanding of disease genetics and pathophysiology, and experienced translational biology. The Role The research associate will be responsible for conducting both
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, computational biology, statistics, or a related field. Strong experience with large language models, including pretraining, fine-tuning, prompt engineering, and evaluation. Knowledge of modern deep learning
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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