Research Assistant/Associate in AI for Materials and Manufacturing

This position at the University of Cambridge’s Department of Engineering offers a Research Assistant/Associate role to advance AI applications in materials and manufacturing. The successful candidate will work on an Advanced Research + Invention Agency-funded project, focusing on protein-guided, droplet-based routes to create multi-functional architected materials. The role involves designing uncertainty-aware hybrid digital twins and AI control architectures, integrating protein descriptors, physics-informed models, and multimodal metrology. Collaboration with seven research groups across protein design, nanocrystal synthesis, and battery technologies will be central to the work, with contributions expected in data integration, model validation, and process optimization. The position is ideal for applicants with expertise in machine learning, statistical modeling, and interdisciplinary research in physical systems.

• Develop an uncertainty-aware hybrid digital twin and AI control architecture for protein-guided, droplet-based manufacturing processes.
• Combine protein and formulation descriptors with reduced-order or physics-informed models for droplet and deposition simulations.
• Integrate multimodal in-line metrology data and process-structure-property machine learning models.
• Design information-efficient experiment selection strategies to diagnose process drift and recommend quantitative parameter corrections.
• Collaborate with researchers across protein design, nanocrystal synthesis, flow chemistry, printing, biomineralisation, magnetics, batteries, and scale-up.
• Establish shared data and model interfaces, ensuring seamless integration of multimodal metrology and effective communication of model limitations.
• Contribute to project reports, publications, presentations, and programme stage gates.