269 parallel-computing-numerical-methods-"https:" positions at Oak Ridge National Laboratory
Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
Methods and Dynamics (MMD) Group at Oak Ridge National Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to numerical methods for kinetic equations. Mathematical
-
Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement
-
to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
-
to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
-
advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
-
computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
-
Methods and Dynamics (MMD) Group at Oak Ridge National Laboratory (ORNL) is seeking several qualified applicants for postdoctoral positions related to Computational Fluid Dynamics. Mathematical topics
-
Requisition Id 15618 Overview: We are seeking a Computational Condensed Matter Physicist who will perform state-of-the-art numerical simulations of quantum magnets (i.e., quantum spin systems
-
Requisition Id 15695 Overview: We are seeking a mid-level Computing Control Room Operator who will focus on responding to upset conditions in the data centers. This position resides in the HPC
-
physics (HEP) detectors, neuromorphic computing, FPGA/ASIC design, and machine learning for edge processing. The successful candidate will work with a multi-institutional and multi-disciplinary team