14 distributed-system Postdoctoral positions at Oak Ridge National Laboratory in Postdoctoral
-
harsh environment applications. Develop data acquisition and control systems that include hydraulic or pneumatic actuation and pressure monitoring combined with interrogation of optical fiber-based
-
Requisition Id 16764 Overview: The National Center for Computational Sciences (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral research associate in the area of
-
for quantum advantage. Research and develop quantum runtime technologies for heterogeneous QHPC distributed systems, including workload mapping, scheduling, resource management, execution orchestration, and
-
workflows to enable AI-readiness at scale. You will work on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed
-
efforts across scientific systems. Develop and apply federated learning on distributed and heterogenous datasets. Develop more efficient and resilient DP techniques that minimize performance loss while
-
Requisition Id 16765 Overview: The National Center for Computational Sciences (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral research associate in the area of HPC
-
simulation tasks. Develop and maintain HPC-ready software workflows for distributed training, large-scale inference, scalable data ingestion, and data management on leadership-class computing systems and
-
of distributed-memory systems and many‑core architectures. Preferred Qualifications: Formal Methods: Experience with automated reasoning or verification tools (e.g., Z3, TLA+, LEAN) to ensure correctness
-
Requisition Id 16867 Overview: We are seeking a Postdoctoral Research Associate in Robotics and Navigation who will focus on developing an autonomous underground exploration and mapping robotic
-
Requisition Id 16723 Overview: We are seeking a Postdoctoral Research Associate with expertise in artificial intelligence (AI) and machine learning (ML) for multiscale physical systems