Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
strategic management and strict adherence to security protocols. We are looking for candidates with extensive experience in either classified HPC data center operations, architecture, parallel computing
-
at the intersection of science and business, enjoy building relationships, and want to make a tangible difference in energy, security, and sustainability, this is your opportunity. Major Duties/Responsibilities Lead
-
environments. Leverage cloud object storage (e.g., Amazon S3) for data staging and artifacts; implement parallel, secure data movement and lifecycle policies. Basic Qualifications: Ph.D. in Computer Science
-
systems. Expertise with batch schedulers (SLURM, PBS, LSF) and parallel file systems (Lustre, GPFS/Spectrum Scale). Proven ability to lead technical projects from concept through implementation, balancing
-
systems, high-speed parallel file systems, and archival solutions critical to advancing scientific discovery and innovation. As part of ORNL’s leadership-class computing ecosystem, you will play a vital
-
batch schedulers (e.g., SLURM, PBS, LSF) and parallel file systems (Lustre, GPFS/Spectrum Scale). Experience implementing and managing automation and configuration management frameworks (Ansible, Puppet
-
power electronics resources modeling, explore different intelligence algorithms to enhance ease of usage of simulations, and different applications of EMT simulations. Selection will be based
-
work environment. If you are passionate about making a difference in the nuclear field and are eager to contribute to groundbreaking research, we encourage you to apply. ORNL offers competitive pay and
-
. Scalability of Preprocessing Pipelines: Design and implement automated, parallel preprocessing workflows capable of handling multi-petabyte datasets efficiently while reducing throughput bottlenecks. Data
-
in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte