Date PostedDecember 13, 2021
Computational Machine Learning Researcher
Albany, OR 97321
Looking for an opportunity to make an impact?
The Leidos Research Support Team supporting the National Energy Technology Laboratory (NETL) is seeking a Computational Machine Learning Researcher to join our program in Albany, OR. This opportunity will allow side by side execution of research with world-class scientists and engineers using state of the art equipment to contribute to new areas of basic and applied research.
Driven by our talented workforce, the Integrated Missions Operation builds trust through an array of energy-related IT, environmental science and engineering solutions to meet our customers’ needs.
If this sounds like the kind of environment where you can thrive, keep reading!
Location: Albany, OR
The objective of this project is Machine Learning composition-properties relationship of RHEAs including temperature-dependent yield strength and room temperature ductility. The Computational Machine Learning Researcher will be responsible for data collection and curation analysis, ML algorithms development, and properties prediction of new alloys using ML.
This opportunity involves collaboration among National Laboratories, the Department of Energy (DOE), universities, and industries.
- Ph.D. degree in Physics, Chemistry, Materials Science, Chemical or Mechanical Engineering, or a related field.
- Experience with high throughput screening.
- Experience in developing predictive and inverse ML models (RF, SVM, NN, etc.) for materials discovery.
- Coding experience with Python, MATLAB, Linux, etc.
- Solid background in phase transformation, thermodynamics, and physics of metallic materials (in particular, High entropy alloys, and high strength steels).
- Excellent oral and written communication skills.
- Excellent record of peer-reviewed quality publications.
- Experience in supervised and unsupervised machine learning.
- Ability to work independently and with minimum supervision.
- Ability to work effectively as a part of a team in a multi-disciplinary environment and interact with people with a variety of expertise.