AMBER Postdoctoral Fellowship: Development of AI-assisted sample optimisation workflows for cryo-EM
Core
Developing AI-assisted workflows to optimize cryo-EM sample preparation and vitrification using the EasyGrid automated system and a new metadata database.
Role type
Postdoctoral Researcher (AI/ML in Cryo-EM)
Builds
Automated, data-driven optimization pipelines for cryo-EM sample preparation integrated with structural biology resources (EMPIAR, EMDB, PDB).
Domain
Structural Biology / Cryo-Electron Microscopy / Artificial Intelligence
Deliverable
production ML models | infrastructure
Required skills
Machine learning, cryo-EM sample preparation, data analysis, database integration, scientific writing
Preferred skills
Experience with EasyGrid or similar automated systems, knowledge of EMPIAR/EMDB schemas
Technologies
EasyGrid, EMPIAR, EMDB, PDB, Python (implied by ML/AI context)
Responsibilities
Develop AI models for sample optimization, integrate EasyGrid database with structural biology resources, collaborate with conceptors and super users of EasyGrid, act as liaison between Grenoble and EBI.
Seniority
Postdoctoral Fellow (0-8 years post-PhD)
