Sort by
Refine Your Search
-
collaborative and open environment? If so, the Oak Ridge National Laboratory’s Learning Systems Group within the Data and Artificial Intelligence Systems section invites you to apply to our new postdoctoral
-
modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
-
Requisition Id 15885 Overview: We are seeking a Postdoctoral Research Associate – Simulation and Machine Learning for Composite Manufacturing who will focus on developing physics-based simulation
-
strengths in any of these areas — quantitative imaging, modeling/transport science, machine learning, or scientific programming — are encouraged to apply. Major Duties/Responsibilities: Lead energy‑storage
-
) with questions related to this position. Major Duties/Responsibilities: Develop and apply machine learning models (ML) as surrogates for high-resolution process-based hydrologic models. Design and
-
such as federated learning. Provenance and Reproducibility Frameworks: Build systems that enable detailed provenance tracking, schema validation, and auditable workflows to ensure trustworthy and
-
thermomechanics. Major Duties/Responsibilities: Help to develop and apply physics-based and/or machine learning models for advanced manufacturing processes. Author peer reviewed papers for journals and conference
-
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
-
-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
-
programming. Incorporate feedback from compiler/runtime systems, including MLIR-based ecosystems (Mojo, Julia, Rust). Compiler Infrastructure & LLVM/MLIR Integration: Extend LLVM/MLIR pipelines to support AI