Material Science and Machine Learning Post-Doctoral Researcher

Description

Job Description:

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 this project is to accelerate alloy design and processing optimization of cost-effective structural materials for extreme environments (e.g. high temperature, high stress, oxidation, corrosion, or hydrogen) using physics-informed machine learning. The materials properties include but are not limited to mechanical properties (yield and fracture stresses, ductility, fracture toughness, fatigue, creep), kinetics (e.g. diffusion), and environmental properties (e.g. oxidation, corrosion, hydrogen embrittlement). Materials processing include but are not limited to casting, welding, and additive manufacturing. Active interaction with team members on multiscale modeling and experiments is expected. This opportunity involves collaboration among national laboratories of Department of Energy, universities and industries.

Locations: Albany, OR; Morgantown, WV or Pittsburgh, PA

Minimum Qualifications:

• M.S. or Ph.D. degree in Computer Science, Statistics, Physics, Chemistry, Materials Science, or a related field
• Demonstrated proficiency in supervised and unsupervised machine learning (e.g. neural network, variational autoencoder, generative learning, adaptive learning, etc.).
• Demonstrated proficiency in computer programming and code development using Python, R, Java, C/C++, Linux script, Fortran, parallel computing, etc.
• Excellent oral and written communication skills.
• Excellent record of peer-reviewed quality publications.

Preferred Qualifications:

• Experience in multi-objective optimization.
• Experience in materials discovery or manufacturing parameter optimization.
• 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: