Postdoctoral Research Associate - Application-driven Composable Distributed Storage

About the role

The National Center for Computational Sciences (NCCS) at the Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral research associate in the area of Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High-Performance Computing (HPC), scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with signature strengths in high-performance computing and data analytics with applications in a large variety of science domains. NCCS is home to some of the fastest supercomputers and storage systems in the world.

Specific areas of research interest

• Flexible, composable storage service architectures that expose useful data container abstractions and interfaces for applications to convey their requirements for I/O performance and data atomicity, consistency, durability, and retention.
• Intelligent and automated selection and composition of data and storage service capabilities and interconnection topologies that simplify the use of diverse distributed storage resources through advanced methods for distributed data placement, layout, tiering, and movement.
• Design and evaluation of approaches for time-sensitive or data-intensive processing of data originating at scientific edge systems using large-scale HPC/AI computational and storage systems.
• Design and evaluation of ephemeral, user-configurable, and composable data and storage systems.
• Evaluation of cloud data storage and analysis solutions (e.g., key-value stores, object or document storage, graph analytics systems) deployed on HPC computational and storage systems.

Duties and responsibilities

• Collaborate with internal and external researchers on a variety of data and storage related research projects for use cases in HPC, scientific AI, and scientific edge computing.
• I/O and storage performance characterization of HPC and scientific AI applications or libraries on multi-tier HPC storage systems.
• Design and evaluation of approaches for time-sensitive or data-intensive processing of data originating at scientific edge systems using large-scale HPC/AI computational and storage systems.
• Design and evaluation of ephemeral, user-configurable, and composable data and storage systems.
• Evaluation of cloud data storage and analysis solutions (e.g., key-value stores, object or document storage, graph analytics systems) deployed on HPC computational and storage systems.
• Co-authorship of peer-reviewed publications, technical reports, and presentations.
• Serve as a mentor for student internships.

Qualifications

• A PhD in computer science/engineering or relevant area with an education and a research track record in HPC/AI/edge systems and storage research within past five years.

Preferred qualifications

• Ability to work independently to design and deploy methods at scale.
• Experience in HPC and associated software development for applications, middleware, and/or system software.
• Flexibility to adapt to diverse R&D projects and tasks.
• Effective communicator in both verbal and written forms.
• Ability to collaborate with scientists, engineers, and sponsors.
• Interest in mentoring student internships.

Special requirements

• HSPD-12 PIV badge: This position requires the ability to obtain and maintain an HSPD-12 PIV badge.