55 phd-computer-artificial-machine-human Postdoctoral positions at Oak Ridge National Laboratory
Sort by
Refine Your Search
-
in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
-
optimization, and application-driven performance analysis for HPC, scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with signature
-
-Performance Computing (HPC), scientific Artificial Intelligence (AI), and scientific edge computing. We are a leader in computational and computer science, with signature strengths in high-performance computing
-
Qualifications: PhD in chemistry, physics, computer science, materials science, or a related field with no more than five years of postdoctoral experience Preferred Qualifications: Experience in energy‑storage
-
Programming experience in scientific computing environments Preferred Qualifications: Experience developing Finite Element Method or CFD models for composite manufacturing applications Knowledge of machine
-
timely manner Maintain strong commitment to the implementation and perpetuation of ORNL core values and ethics Basic Qualifications: A PhD related to computational or theoretical physics, chemistry
-
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
-
: https://www.ornl.gov/content/research-integrity Basic Qualifications: To be eligible you must have completed a PhD in chemistry, physics, engineering, or a related field with in the last 5 years
-
toward integration of hydropower with battery storage and other technologies. Computational and analytical skills : Demonstrated ability in selecting and deploying machine learning tools (Random Forest
-
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 kinetic monte