Scientist - Ensemble Structural Informatics

Position Summary

The diffUSE Project is seeking a Scientist to join a multidisciplinary team to help build the infrastructure needed to host and distribute dynamic structural biology data. The diffUSE Project is an ambitious initiative designed to advance our understanding of protein dynamics by building the experimental methods, computational models, and global infrastructure needed to capture molecular motion at scale. Our goal is to establish dynamic structural biology as a foundational pillar of modern science, as transformative and indispensable as static structures have been. This role will lead the development of standards that empower the structural biology community to deposit, validate, search, and leverage dynamic structural information at scale.

Duties

• 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
• Work 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

Compensation

The posted salary range is based on location in the Bay Area. The successful candidate will receive a competitive compensation package, commensurate with their experience and location.