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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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biology, bioinformatics, genomics, statistics, physics, or a related quantitative field Demonstrated ability to analyse large-scale sequencing data using R, Python or equivalent A self-motivated team player
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disease. Key responsibilities Lead and conduct the processing and statistical analysis of large-scale long-read RNA and DNA sequencing, single nuclei RNA sequencing and spatial transcriptomics
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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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the expertise unit into a dedicated team over time. Your profile PhD in machine learning, computer science, computational biology, statistics, or a related field. Demonstrated scientific excellence and leadership
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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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. Create interactive portals to visualize and probe the data for your colleagues and the community. Ensure that the data is analyzed in a reproducible and statistically sound way. Troubleshoot and resolve