This requires an advanced software infrastructure that ties together robotic systems, AI-driven decision-making, experiment orchestration, and scientific data management into a coherent, scalable platform. Within this infrastructure, the software backend ensures that all components communicate reliably, workflows execute reproducibly, and the system can scale with the institute's growing research needs. * Implement and maintain the core software platform for self-driving lab systems, including the service layer, internal APIs, and data flow between subsystems * Build and maintain the workflow orchestration backend that schedules, coordinates, and monitors automated experiment execution across robotic and computational systems * Ensure platform reliability, fault tolerance, and reproducibility — including logging, error handling, and system state management at scale
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