Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
Requisition Id 15721 Overview: We are seeking a Postdoctoral Research Associate who will contribute to the development and implementation of novel quantum algorithms for materials simulation, with
-
Ridge National Laboratory. The SDS Group consists of researchers focused on delivering computational solutions for the study of complex engineered systems using scalable architecture-aware algorithms
-
in advanced machine learning algorithms. This position resides in the Manufacturing Systems Analytics (MSA) Group in the Manufacturing Science Division (MSD), Energy Science and Technology Directorate
-
and numerical algorithms for modeling and simulation of nuclear systems. Computational Nuclear Engineers within the RTHPCM group will work with group, section, and division members and external
-
, enhanced sampling, QM/MM) Experience improving performance and scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation
-
system integration, as well as embedded controller development. Experience in modeling converter topologies and distribution grids, developing control algorithms, and implementing controller code is
-
and numerical algorithms for modeling and simulation of nuclear systems. Computational Nuclear Engineers within the RTHPCM group will work with group, section, and division members and external
-
-class S&T products for sensitive national security missions. The selected candidate will support research efforts in signal processing and analysis, with an emphasis on the development of novel algorithms
-
scalability of simulation workflows via: Parallelization and performance engineering GPU/accelerator optimization Algorithmic innovation Experience applying machine learning or AI to molecular simulation
-
: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance