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Work at the interface of protein engineering and toxin biology; develop antibodies, nanobodies, and binding proteins with condition-dependent activity; combine experimental discovery approaches with data-driven methods; collaborate with leading international partners; design, discover, and characterize antibodies and nanobodies whose binding changes with environmental conditions; take ownership of the research project; generate experimental data and link them to environmental factors influencing protein–protein interactions.
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PhD in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field; strong theoretical and practical experience in deep learning; hands-on experience developing generative models; highly proficient in Python, PyTorch and/or JAX; experience training large-scale neural networks on HPC or GPU clusters; experience with representation learning and sequence or structural modeling; strong publication record in relevant AI/ML venues or interdisciplinary journals; ability to work independently and drive technical innovation; ability to manage projects and mentor students; advantage if experienced with protein language models (e.g., ESM, ProtT5), antibody-specific language models (e.g., AntiBERTy, AbLang2), structure prediction frameworks, geometric deep learning or graph neural networks, computational antibody/binder discovery or molecular modeling, MLOps, reproducible ML pipelines, and scalable AI infrastructure.
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