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and propose meaningful and testable hypotheses, grounded in disease biology. Perform end‑to‑end processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g
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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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, that combines diffusion and transformer models, there are clear indications that the analysis of this data can be automated. This will open new avenues in data interpretation and building predictive models
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analysis Background in biomedicine and digital pathology What we offer Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within
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; support multi‑omics data integration and analysis across multiple research groups; and collaborate on the development and maintenance of computational pipelines for spatially resolved transcriptomics and
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Maintaining consumables stocks necessary for the above two activities Basic maintenance of peptide synthesizer, LCMS and HPLC Biophysical analysis of proteins Documentation of experimental
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Biology (VIB.AI), more specifically in the Ghent branch of VIB.AI. Profile text Academic education You contribute to various lecturers in the discipline of bioinformatics, data analysis of large-scale (bio
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the discipline of bioinformatics, data analysis of large-scale (bio)medical data, applications of artificial intelligence and machine learning. You contribute to high-quality teaching in bachelor and master years
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who thrives in a collaborative international environment Excellent communication skills in English (written and spoken) Nice to have: Experience with cancer genomics, single-cell analysis, or long-read
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Amplicon sequencing analysis Grant and manuscript writing Guide students and work with technicians Profile Candidates must have a PhD in (Plant) Biotechnology, Molecular Biology, Biochemistry or equivalent