We are seeking an accomplished Computational Protein Scientist to apply machine learning and computational approaches to therapeutic antibody discovery and engineering.
Working at the interface of data science, computational biology and experimental research, the successful candidate will develop and validate models that support the design, selection and optimisation of therapeutic antibodies and other protein-based molecules.
This collaborative role requires strong scientific judgement, a sound understanding of antibody structure and function, and the ability to translate computational outputs into experimentally testable recommendations.
Key responsibilities:
- Develop, train, validate and evaluate machine-learning models using internal and external biological datasets
- Apply machine learning, statistical modelling and computational biology approaches to antibody discovery, design, optimisation and candidate selection
- Partner with experimental scientists to define key scientific questions and convert them into robust computational strategies
- Analyse antibody sequence, structure, binding, functional and developability data to support optimisation of affinity, specificity, stability, solubility and manufacturability
- Design validation strategies, assess prospective model performance and use experimental results to improve subsequent design cycles
- Contribute to computationally guided library design, lead optimisation and data-led project decision-making
- Assess emerging methods in AI, protein language models, generative design and structure prediction for relevance to antibody engineering
- Communicate model outputs, uncertainty, limitations and recommendations clearly to multidisciplinary project teams
- Maintain high standards of data quality, reproducibility, documentation and scientific integrity
- Build productive collaborations across computational, protein-engineering, discovery and development teams, sharing expertise as appropriate
Essential experience and qualifications:
- PhD, or equivalent research experience, in machine learning, computational biology, bioinformatics, protein engineering, biophysics or a related discipline
- Relevant postdoctoral or industry experience applying computational methods to biological research
- Demonstrable experience developing, validating and applying machine-learning models to complex biological datasets
- Strong understanding of antibody or protein sequence, structure and function
- Experience with data preparation, quality assessment, statistical analysis and reproducible scientific computing workflows
- Proficiency in Python and relevant machine-learning or scientific-computing tools
- Ability to assess complex scientific evidence and make clear, data-led recommendations
Desirable experience
- Direct experience in therapeutic antibody discovery, engineering or developability assessment.
- Experience with protein language models, generative modelling, structure prediction, sequence design or computer-aided protein-design software
- Knowledge of molecular modelling, docking, molecular dynamics or free-energy calculations.
- Understanding of phage, yeast or mammalian display technologies and associated high-throughput screening or sequencing datasets
- Experience integrating computational design with experimental design–build–test–learn cycles
- Familiarity with cloud computing, version control and reproducible model-development workflows
- Experience within pharmaceutical or biotechnology research