-
specializing in artificial intelligence for science, knowledge-guided machine learning, and scalable scientific AI workflows. The successful candidate will conduct research at the intersection of machine
-
discovery. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially
-
, machine learning, and statistical modeling methods for multimodal manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and
-
teams and community partners to ensure outputs reflect local priorities and inform adaptation planning. Duties may include: Develop spatially explicit computational models using machine learning
-
Requisition Id 16802 Overview: We are seeking a Postdoctoral Research Associate for the development and application of advanced multiphysics simulations, and machine learning (ML) methods relevant
-
opportunity to work 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
-
expertise in multiscale and multifidelity simulation techniques ( ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or
-
seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in
-
regulation/chromatin biology, computational genomics, human genetics, functional genomics, genome editing, or machine learning for genomic data is particularly relevant. Prior experience in sickle cell
-
lasers, optics, and optical emission spectroscopy. Experience designing custom experimental hardware, vacuum systems, and plasma diagnostics. Experience applying machine learning methods for