The Leidos Research Support Team supporting the National Energy Technology Laboratory (NETL) is seeking a Computational Materials Post-Doctoral Researcher to join our as part of our Workforce Development Program. 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.
The objective of the research team is to accelerate the design and development of cost-effective high-performance advanced structural materials such as high entropy alloys, Ni- and Fe-based alloys for extreme environments (e.g. high temperature, high stress, oxidation, corrosion, or hydrogen) using multiscale computational modeling and machine learning. The research will focus on predicting thermodynamic (e.g. phonon, entropy), kinetic (e.g. diffusion), defect energetics (stacking faults, twining, anti-phase boundary, surfaces, interfaces and grain boundaries), mechanical properties (e.g. elasticity, plasticity, ductility, yield stress), and environmental properties (e.g. oxidation, corrosion, or hydrogen embrittlement) of alloys using DFT and machine learning as well as other modeling techniques. The computational outcome will be used to guide experimental effort in optimizing the composition-processing-microstructure-properties relationships. This opportunity involves collaboration among national laboratories of Department of Energy, universities and industries.
Locations: Albany, OR; Morgantown, WV or Pittsburgh, PA
- Ph.D. degree in Physics, Chemistry, Materials Science, Chemical or Mechanical Engineering, or a related field.
- Demonstrated proficiency in DFT calculations on diffusion, phonon, and mechanical properties using VASP.
- Demonstrated proficiency in computer programming and code development using Python, C/C++, Fortran, Linux script, parallel computing, etc.
- Excellent oral and written communication skills.
- Excellent record of peer-reviewed quality publications.
- Experience in supervised and unsupervised machine learning.
- Experience in molecular dynamics using LAMMPS to simulate mechanical behavior of solids.
- 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.
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