Computational Structural Biologist

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This is a unique opportunity to directly shape our induced proximity computational platform, from defining the structural and biophysical data needed to answer key scientific questions, through interpretation, to translating the resulting insights into improved predictive ML models. Molecular glues work by stabilising protein-protein interactions that are often weak, transient, or difficult to observe directly. Predicting when these interactions will form productive complexes — and how they translate into cooperativity, affinity, and downstream biological effect — remains a major unsolved challenge. In this role, you will help build the computational and data foundations to make these interactions more predictable, working across our data generation strategy, structural interpretation, model validation, and iterative improvement of our next-generation ML models for induced proximity. Key responsibilities Own computational structural biology projects end-to-end, independently translating requirements into validated, production-grade workflows with full autonomy. Develop computational approaches for induced proximity, with a focus on modelling weak or transient protein-protein interactions, and turn promising prototypes into robust, reusable, and well-tested code. Apply machine learning, co-folding, protein docking, and molecular dynamics to integrate experimentally derived priors into predictions of structure, cooperativity, and binding affinity. Query structural and sequence databases, and build large-scale data pipelines from the ground up for model training, benchmarking, and evaluation. Interpret structural and biophysical data, and translate biological insights into our next-generation ML models for induced proximity. Partner with experimental scientists to design data collection and validation strategies. What you will bring PhD or MSc in Structural Biology, Biochemistry, Biophysics, Chemistry, Bioinformatics, or a related field. Possess experience across academia and at least 2 years of industry experience (preferably biotech). Strong understanding of structural and biophysical datasets, including cryo-EM, XL-MS, and SAXS, with experience in data collection, analysis, and structure determination. Strong programming skills in Python, with experience in CI/CD and cloud infrastructure (e.g., GCP, AWS). Experience building reproducible and scalable computational/structural biology workflows. Experience applying modern protein structure prediction / co-folding approaches (e.g., AlphaFold, Boltz, Chai-1) and/or downstream methods (docking, molecular dynamics). Excellent communication and documentation skills, with a strong bias for reproducibility and collaboration. A proactive, delivery-oriented mindset and a passion for enabling cutting-edge research through scalable systems. Nice to have Experience with induced proximity modalities, such as PROTACs, molecular glues, and other bifunctional molecules. Prior exposure to industry drug discovery projects involving structure-based design, protein-protein interaction modulation, targeted protein degradation, or comparable modalities. Experience working with cloud-based infrastructure. Experience with agile project management. Why join Bindbridge Competitive salary (plus equity commensurate with experience); support for conference attendance, publications, and patents; fully remote work, with quarterly on-site meetings. Be part of an early team shaping AI-driven molecular discovery in agriculture and contribute to global food security For more information and how to apply please go to the job page Cambridge, United Kingdom Computational Structural Biologist 17COUNTRIES & MEMBERS 11CENTRES 29FACILITIES 104SERVICES 13089 USERS 2214 PUBLICATIONS Instruct-ERIC Hub, Oxford House, Parkway Court, John Smith Drive, Oxford OX4 2JY +44 (0) 1865 98 86 39 | support@instruct-eric.org | Acknowledgements | Powered by ARIA Terms of Use | Terms of Submission | Privacy Policy | Access Policy | Copyright © 2026 The University of Oxford This platform benefited from services and the resources provided by EGI, with the dedicated support of IN2P3-IRES