23 combinatorial-optimization Postdoctoral positions at Oak Ridge National Laboratory in United States
-
the development and optimization of products for a variety of industries from automotive to aerospace made from new bio- and waste-derived plastic resins and fillers. The ideal candidate for this role would be
-
with the world's first exascale system, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The successful candidate
-
supercomputer, the world's first exascale computing system. This is a unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and
-
researcher to join the Workflow Systems Group and help advance the use of AI in scientific discovery. This position centers on scientific machine learning, automated AI/ML optimization, and high-performance
-
for radioactive s-, p-, and f-block metal ions. Independently execute and troubleshoot complex, multi-step organic syntheses on the multi-gram scale, including reaction optimization, scale-up, purification, and
-
. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
-
. Optimize system and component designs for performance and safety. Develop agentic workflows for scientific computing. Author peer-reviewed papers, technical reports for internal and external release and
-
to additive manufacturing (AM), virtual manufacturing, material characterization, topology optimization, and real-time sensing. This position resides in the Computational Sciences and Engineering Division (CSED
-
, conduct experimental campaigns, perform materials synthesis and analysis, sub-component fabrication, process optimization and integration, and prepare technical documents and research publications. Present
-
such as quantum and analog computational models. You will explore how compilers, runtimes, and AI-driven agents can co-optimize complex architectures, reasoning across conventional processors (CPUs/GPUs