Scientist - Ensemble Structural Informatics
Leads development of standards and validation frameworks for dynamic structural biology data, focusing on ensemble models from X-ray crystallography and cryo-EM. Collaborates with engineers to build deposition, search, and retrieval infrastructure for the diffUSE Project. Requires PhD in structural biology or related field.
About the job
Key Responsibilities
- Oversee development of ensemble-aware validation frameworks that assess fit-to-data, physical realism, and uncertainty across diverse structural representations
- Identify and prioritize technical challenges, from data representation to validation frameworks
- Guide the creation of data deposition, search, and retrieval tools that allow users to interrogate and interpret structural heterogeneity at scale
- Help coordinate with stakeholders to ensure interoperability and adoption
- Work with software developers, data engineers, and user experience designers to translate scientific requirements into robust technical solutions
Required Skills and Qualifications
- Ph.D. in structural biology, biophysics, computational biology, or related field
- Demonstrated expertise in structural biology methods
- Deep understanding of structural heterogeneity and dynamics in biomolecular systems
- Experience with data standards, metadata frameworks, or scientific database development
- Strong collaborative skills and ability to build consensus across diverse scientific communities
Preferred Skills and Experience
- Experience with PDB, EMDB, BMRB, or other structural biology databases
- Knowledge of validation methods for experimental and computational structural data
- Familiarity with machine learning workflows and ML-ready data formats
- Background in model uncertainty quantification or ensemble refinement methods
- Understanding of software development practices and data engineering principles
- Track record of working at the interface of methods development and infrastructure
Skills
X-Ray Crystallography, Cryo-Em, Pdb, Emdb, Bmrb, Ensemble Models, Multistate Models, Validation Metrics, Data Standards, Metadata Frameworks, Machine Learning, Uncertainty Quantification, Ensemble Refinement, Software Development, Data Engineering
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