ML Scientist: Antibody Discovery & Design

Barrington James; Cambridgeshire and Peterborough; Sur place; GBP 45 000 - 65 000; Plein temps

Résumé du poste

Barrington James in Cambridge is seeking a Scientist to apply machine learning and computational biology to antibody discovery, engineering and optimisation. You will develop models using internal and external datasets and collaborate with experimental scientists to turn results into testable design recommendations for therapeutic antibodies and proteins.

This is an great opportunity for an early-career computational scientist to build skills in a collaborative, multidisciplinary R&D environment.

Qualifications

• PhD in ML, computational biology, bioinformatics, protein engineering, biophysics or related field, or MSc with 1–2 years of relevant experience.
• 1–2 years applying computational or machine-learning methods to biological research.
• Hands-on experience developing, validating and interpreting ML models.
• Proficiency in Python and relevant scientific computing tools.
• Strong analytical, problem-solving and communication skills.
• Ability to work in multidisciplinary teams and deliver project objectives.

Responsabilités

• Develop, train, validate and evaluate ML models using internal and external biological datasets.
• Apply machine learning, statistical modelling and computational biology to antibody discovery, design, optimisation and candidate selection.
• Analyse antibody sequence, structure, binding, functional and developability data to support optimisation of affinity, specificity, stability, solubility and manufacturability.
• Collaborate with experimental scientists to define scientific questions, design validation strategies and use experimental results to refine models and design cycles.
• Contribute to computationally guided library design, lead optimisation and project decision-making.
• Keep up to date with advances in artificial intelligence, protein language models, generative design and structure prediction.
• Communicate model outputs, uncertainty, limitations and recommendations clearly to multidisciplinary project teams.
• Maintain high standards of data quality, reproducibility, documentation and scientific integrity.

Connaissances

Python

Machine learning

Computational biology

Data analysis

Team collaboration

Communication

Formation

PhD in ML / computational biology / bioinformatics / protein engineering / biophysics

MSc with 1–2 years relevant research/industry experience

Outils

Rosetta

Schrödinger

Chemical Computing Group suite

Cloud computing platforms

Description du poste

Barrington James in Cambridge is seeking a Scientist to apply machine learning and computational biology to antibody discovery, engineering and optimisation. You will develop models using internal and external datasets and collaborate with experimental scientists to turn results into testable design recommendations for therapeutic antibodies and proteins.

This is an great opportunity for an early-career computational scientist to build skills in a collaborative, multidisciplinary R&D environment.