Thermodynamic Simulations Engineer

Leidos is seeking a Thermodynamic Simulations Engineer to work with a distributed, multidisciplinary team working on the cutting edge hydrogen energy infrastructure and transportation.  The candidate should possess knowledge of and practical experience with Machine Learning in a structural materials application. The position will be primarily offsite research and development support for the National Energy Technology Laboratory (NETL) team in Albany, OR. This work will involve a multi-disciplinary, scientific, and technically-oriented national laboratory team, participation alongside other data scientists, engineers, geologists, and computer scientists that produces technological solutions for America’s energy challenges. From developing creative innovations and efficient energy systems, to advancing technologies that produce, transport, and utilize hydrogen and other fuels, NETL research is providing breakthroughs and discoveries that support home-grown energy initiatives, stimulate a growing economy, and improve the health, safety, and security of all Americans.

Primary Responsibilities:

• Perform data collection, thermodynamic modeling, and machine learning to support the design of the hydrogen-resistant pipeline steel.
• Optimize pipeline steel properties, including but not limited to composition, processing parameters, microstructure, and corresponding performance in hydrogen.
• Simulate hydrogen diffusivity and mechanical response during tensile, fracture, and crack growth testing in hydrogen.
• Evaluate effective hydrogen diffusivity, hydrogen trapping, hydrogen embrittlement, and intrinsic ductility of high strength steel and generate new data using these generated models based on literature.

Required Education & Experience:

• PhD degree in Physical Chemistry, Material Science, Computer Science, or a related field.
• Demonstrated proficiency in data collection, curation, and quality assessment.
• Background in structural materials and capable of performing thermodynamic calculations.
• Demonstrated proficiency in supervised and unsupervised machine learning.
• Demonstrated proficiency in computer programming and code development using codes such as Python, Java, C/C++, Fortran, Linux script, etc.
• Ability to work independently and with minimum supervision.
• Excellent oral and written communication skills
• Ability to work effectively as a part of a team in a multi-disciplinary environment and interact with people with a variety of expertise
• U.S. Citizen
• Ability to obtain access to NETL systems

Preferred Qualifications:

• Demonstrated proficiency with deep learning such as Gaussian process, neural network, graph neural network, variational autoencoder, generative learning and transfer learning.
• Research experience in high throughput CALPHAD calculations.
• Research experience in physics-informed multi-objective optimization.
• Research experience in composition-processing-microstructure-properties relationship.
• Research experience in physics-informed multi-objective optimization.
• Excellent record of peer-reviewed quality publications in machine learning.

Salary Range for this position: $110K to $115K

Original Posting Date:

2024-07-24

While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:

Pay Range $81,250.00 - $146,875.00

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.