Post-Doctoral Researcher - Computational Materials


Job Description:

Looking for an opportunity to make an impact?

The Leidos Research Support Team supporting the National Energy Technology Laboratory is seeking a Computational Materials Post-Doctoral Researcher to join us 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.

TERM OF COMMITMENT: This research position is intended to be a term position, with varying levels of commitment that are not expected to last longer than 2 years. You will be informed prior to applying what length of commitment is anticipated. Also, those who successfully fill a term position, may be invited to apply for an additional term. Nothing in this paragraph is intended to create an employment contract. Employment will remain at will.

Location: Albany, OR

If this sounds like the kind of environment where you can thrive, keep reading!

The objective of this project is to incorporate microstructural aging into the creep and uncertainty quantification (DFT focus), 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, and/or corrosion) using multiscale computational modeling and machine learning. The research will focus on perform DFT calculations on interfacial energy with and without segregants of C, B and N, perform mean-field precipitation kinetics of sigma phase formation and growth in 347H stainless steels, and develop machine learning algorithms and then construct ROM for sigma phase precipitation kinetics in 347H stainless steels. This opportunity involves collaboration among national laboratories of Department of Energy, universities, and industries. 

Required Qualifications:

  • Ph.D. degree in Physics, Chemistry, Materials Science, Chemical or Mechanical Engineering, or a related field.
  • Substantial DFT expertise in calculating crystal defect energetics such as interfaces, solid understanding of diffusion in alloys.
  • 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.

Preferred Qualifications:

  • Experience in supervised and unsupervised machine learning.
  • Experience with data mining of CALPHAD generated data using TC-Python interface.
  • 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.

Pay Range:

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