Loading...
Loading...

Astera Institute is seeking a Research Assistant for its diffUSE Project focused on structural biology data infrastructure. This full-time, in-person role involves maintaining data pipelines, curating datasets, and running validation checks for X-ray, cryo-EM, and ensemble data. The position is ideal for an early-career researcher wanting hands-on experience in a fast-paced, multidisciplinary environment. The candidate should be detail-oriented and motivated by making scientific data more open and reusable.
Astera is a private foundation on a mission to steer science and technology toward an abundant future. We believe the coming years will bring an era of unprecedented scientific and technological advancement as exponential progress in AI converges with central advances in other fields to dramatically accelerate innovation. This inflection point provides an unparalleled opportunity to fundamentally rethink the institutions, systems, and tools that drive scientific progress.
Unlike traditional non-profit research organizations, projects supported by Astera operate like high-velocity startups, allowing us to focus on ambitious goals, match structure to problem, and attract strong technical talent and leadership. You can read more about our mission, vision, and programming here.
The diffUSE Project at Astera Institute/Radial is building open infrastructure for dynamic structural biology, making protein conformational and ensemble data findable, validated, and reusable at scale. The Research Assistant will support this mission by maintaining data pipelines, curating structural datasets, and running validation checks across X-ray, cryo-EM, and ensemble-derived data, working closely with Scientists and software engineers on day-to-day technical execution. This is a full-time, in-person role suited to someone early in their research career who wants hands-on exposure to structural biology data infrastructure in a fast-moving, multidisciplinary environment. The ideal candidate is detail-oriented, comfortable with ambiguity, and motivated by the goal of making scientific data more open and reusable.