Research Assistant/Associate in AI for Materials and Manufacturing

The role

The University of Cambridge is seeking a Research Assistant/Associate to work on a multidisciplinary research programme developing protein-guided, droplet-based routes to architected functional materials, as part of an Advanced Research + Invention Agency-funded project.

What you'll do

• Develop the project's uncertainty-aware hybrid digital twin and AI control architecture
• Combine protein and formulation descriptors, reduced-order or physics-informed droplet and deposition models, multimodal in-line metrology and process-structure-property machine learning
• Develop information-efficient experiment selection, diagnose process drift, recommend quantitative parameter corrections and support route down-selection and statistical process control
• Collaborate closely with researchers in protein design, nanocrystal synthesis, flow chemistry, printing, biomineralisation, advanced characterisation, magnetics, batteries and scale-up
• Help establish shared data and model interfaces, integrate multimodal in-line metrology, communicate uncertainty and model limitations, and contribute to reports, publications, presentations and programme stage gates

What it takes

• PhD in engineering, computer science, applied mathematics, physics, data science, materials science or a closely related discipline
• Essential capabilities include machine learning or statistical modelling for physical systems, quantitative model validation, scientific programming and reproducible data workflows, and effective multidisciplinary collaboration
• Experience in hybrid modelling, uncertainty quantification, active learning or optimisation, digital twins, process control, multimodal metrology data, materials informatics or advanced manufacturing is desirable

What you'll bring

How to apply

Apply directly on University of Cambridge - Department of Engineering's site. We link straight through — no resume parsing, no profile to fill out.

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