Key Responsibilities
Machine learning and data analytics
- Develop ML models for process optimisation, prediction, and control, including yield, CQAs, aggregation, glycosylation, and impurity profiles.
- Apply supervised, unsupervised, and multivariate methods (e.g. neural networks, regression, classification, clustering, PCA/PLS, Bayesian models).
- Integrate omics, analytical (LC-MS, spectroscopy), and process data into unified modelling frameworks.
- Perform feature engineering informed by bioprocess and product knowledge.
Biomanufacturing applications
- Digitially Improve AI driven bioprocesses
- Develop soft sensors and digital twins for real-time or near-real-time process monitoring.
- Replace or augment design of experiments (DOE) with ML-driven process optimisation.
Deployment and validation
- Translate models into production-ready tools (Python/R, APIs, dashboards).
- Perform model validation, versioning, and lifecycle management.
Collaborative environment
- Work closely with process scientists, analytical scientists, engineers, and quality teams.
- Clearly communicate model assumptions, limitations, and impact to non-ML stakeholders.
Required Qualifications
- BSc or MSc in Machine Learning, Data Science, Chemical/Biochemical Engineering, Bioinformatics, Systems Biology, or related field.
- Strong programming skills in Python (e.g. scikit-learn, PyTorch, TensorFlow)
- Solid understanding of statistics, experimental design, and model validation.
Key Competencies
- Strong problem-solving and analytical skills
- Excellent communication across disciplines in English
What We Offer
- Opportunity to apply ML to real-world, high-impact manufacturing challenges
- Collaborative environment at the interface of AI, biology, and engineering
- Exposure to cutting-edge therapeutics and advanced manufacturing platforms