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, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply
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have skills in eukaryotic cell biology, electron microscopy, and bioimage analysis. You have a basic knowledge in integrative structural biology, and in AI / deep learning approaches and/or sub-tomogram
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results-oriented team player. Ideal profile includes following competencies and experience: Strong programming skills, deep statistical knowledge and a proven track record with machine learning Solid
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, 2024, or be on track to complete all PhD requirements by the expected start date of October 15, 2026. Demonstrated expertise in modern AI/ML, including deep learning and hands-on experience with
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experience with deep learning (DL) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence
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research at the intersection of AI/deep learning, bioinformatics, mathematical modeling, computational topology, scientific machine learning, for systems biology, single-cell genomics and protein design. The