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, technical depth, and a strong track record of applied research in Computational Biology, Structural Biology, Protein Engineering, Machine Learning, or a closely related field. Strong understanding and
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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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are looking for a team player who is open to learn about the newest technologies in single-cell and spatial transcriptomics and flow cytometry. Key Responsibilities Collaborate with PhD students, postdoctoral
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interdisciplinary work at the interface between neurodegeneration, modeling, screening and machine learning Prior experience in iPSC modelling and CRISPR tools Our Offer A stimulating, international
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experimental workflows for generating and automating the acquisition of high-quality training datasets for machine learning models. Provide training to students on new technologies, protocols, and best practices
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player and great collaborator Strong interest in interdisciplinary work at the interface between neurodegeneration, modeling, screening and machine learning Prior experience in iPSC modelling and CRISPR
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analysis Embedding within a computational team, with extensive experience in computational biology and machine learning. Embedding within an experimental team, with direct availability of experimental
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experience with data analysis Enthusiastic team player Basic understanding of immunology Desirable but not required Experience in single-cell or spatial data analysis Machine learning experience Key personal