89 parallel-and-distributed-computing "SciLifeLab " Postdoctoral positions at Oak Ridge National Laboratory
Sort by
Refine Your Search
-
on high-performance computing (HPC) environments using distributed data or model parallel training. Experience with multi-physics simulations on HPC and with ML models. Experience working in a multi
-
high-performance computing platforms. Perform large-scale simulations of fission and fusion systems employing models for turbulence, heat transfer, magnetohydrodynamics, etc. Employ finite element
-
areas include: Parallel and distributed graph and or ML algorithms — shortest paths, connectivity, clustering, or graph traversal on GPU clusters and distributed-memory systems. Sparse direct and
-
Sciences (NCCS), Computing and Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL) to perform leading-edge computational materials research and development. The scientific
-
Sciences (NCCS), Computing and Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL) to perform leading-edge computational materials research and development. The scientific
-
workflows for grid modeling, optimal power flow (OPF), surrogate modeling, and data-driven analysis of large-scale electric power system simulations on DOE leadership-class computing resources. The
-
; and one-sided asynchronous programming models. Experience in heterogeneous computing, developing and debugging massively parallel algorithms and code performance profiling are a plus. Special
-
learning, high-performance computing, scientific modeling, and domain-informed AI to accelerate discovery across DOE mission areas such as energy, materials, biology, nuclear science, autonomous
-
motivated and talented postdoctoral research associate(s) to join our dynamic team. This role lies at the exciting intersection of High-Performance Computing (HPC) and Artificial Intelligence (AI) at
-
federated learning SOTA algorithms. Knowledge of distributed optimization and consensus algorithms. Knowledge of large models and hyper-parameter optimization. Knowledge of high-performance computing and its