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.

• 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.

• Modelling mindset
• Strong problem-solving and analytical skills
• Excellent communication across disciplines in English

• 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