AI for Materials & Manufacturing: Research Role

1000scholars; Cambridge; Presencial; GBP 33 000 - 46 000; Tempo integral

Resumo da oferta

University of Cambridge is seeking a Research Assistant/Associate in AI for Materials and Manufacturing, located at West Cambridge. The role focuses on developing a protein-guided, droplet-based route to architected materials, integrating ML-driven models with experimental workflows.

Applicants should have a PhD (or near completion) in a relevant field and strong skills in machine learning, modelling, and reproducible data analysis. Fixed-term appointment; funding until 31 December 2027.

Qualificações

• PhD or near-completion in a relevant engineering/science discipline.
• Strong background in ML/statistical modelling and scientific programming.
• Experience with data workflows, uncertainty quantification and model validation.
• Collaboration across multiple groups and disciplines is essential.

Responsabilidades

• Develop the project's uncertainty-aware hybrid digital twin and AI control architecture.
• Integrate protein descriptors with process models and multimodal metrology data.
• Establish shared data interfaces and model communication across groups.
• Contribute to reports, publications and programme stage gates.

Conhecimentos

Machine Learning

Statistical Modelling

Scientific Programming

Reproducible Data Workflows

Multidisciplinary Collaboration

Hybrid Modelling

Uncertainty Quantification

Active Learning

Optimization

Digital Twins

Process Control

Metrology Data

Materials Informatics

Advanced Manufacturing

Formação académica

PhD in Engineering/CS/Math/Physics/Data Science/Materials Science or related

Descrição da oferta de emprego

University of Cambridge is seeking a Research Assistant/Associate in AI for Materials and Manufacturing, located at West Cambridge. The role focuses on developing a protein-guided, droplet-based route to architected materials, integrating ML-driven models with experimental workflows.

Applicants should have a PhD (or near completion) in a relevant field and strong skills in machine learning, modelling, and reproducible data analysis. Fixed-term appointment; funding until 31 December 2027.