Sort by
Refine Your Search
-
applying the latest advancements in machine learning, high performance computing, and numerical methods to develop computational tools and models that are used for large-scale, physics-based simulations of a
-
Postdoctoral Research Associate - Structural Simulation and Machine Learning (ML) for Polymer Compos
manufacturing technologies through machine learning and physics-based simulations, specifically finite element analysis (FEA) for polymer composites. The candidate will also focus on developing a manufacturing
-
) is seeking qualified applicants for a postdoctoral position in machine learning and surrogate models. Areas of interest include graph neural networks, federated learning, data-driven model reduction
-
Engineering Division (CSED), at Oak Ridge National Laboratory (ORNL) is seeking a Postdoctoral Research Associate to develop and apply scalable artificial intelligence (AI) / deep learning (DL) methods
-
proteins interacting with materials for plastics degradation, metal binding, and material synthesis. You will be expected to apply and develop machine learning approaches for protein design and to integrate
-
education. The chosen candidate is expected to develop and execute research projects on applying advanced machine learning algorithms to applications of second life use of electrochemical energy storage
-
completed in the last five years. Hands-on experience with machine learning, process modelling, and industrial data acquisition systems is very valuable. This position may also require access to technology
-
Requisition Id 12691 Overview: The Manufacturing Systems Analytics (MSA) group at the Oak Ridge National Laboratory (ORNL) conducts applied machine learning and decision science research and
-
with machine learning, particularly as applied to hydrology or other environmental systems Experience developing software for, and running software, in a cluster computing environment Experience with
-
analytics, and machine learning, the Grid Interactive Controls group delves deeply into understanding intricate grid-edge operations. Researchers are dedicated to laying the groundwork for optimal X2G