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Job #: R-00090744
Location: Remote, US
Category: Research Scientist
Schedule (FT/PT): Full Time
Travel Required: Yes, 10% of the time
Shift: Day
Potential for Telework: Yes, 100%
Clearance: None
Referral Eligibility: Ineligible
Group: Civil

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Description

Job Description:

We are seeking a Computational Materials Scientist to join our team in Albany, OR. This position may allow for remote or a hybrid of remote and onsite research and development support for the NETL R&D facility 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 enhance oil and natural gas extraction and transmission processes, 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. Highly skilled women and men at NETL’s three research sites – Albany, Oregon; Morgantown, West Virginia, and Pittsburgh, Pennsylvania – conduct a broad range of research activities that support DOE’s mission to advance the national, economic, and energy security of the United States.

We are seeking a Computational Materials Scientist interested in joining a distributed, multidisciplinary team working on the cutting edge of high-performance computing (HPC) and multiphase flow modeling. This work specifically supports accelerating the design and development of cost-effective high-performance alloys and ceramic materials (e.g. oxides, silicide’s, carbides, nitrides, borides, etc.) for extreme environments (such as high temperature, high stress, water vapor, hydrogen, oxidation, corrosion, erosion, etc.) using multiscale computational modeling and machine learning.

Come join the Leidos Research Support Team supporting the National Energy Technology Laboratory (NETL), where you’ll be able to work side by side conducting research with world-class scientists and engineers using state of the art equipment to contribute to new areas of basic and applied research; discover, integrate, and mature technology solutions to enhance the nation’s energy foundation and protect the environment for future generations. You will contribute to the development of patents, written reports, journal articles, and contribute to presentations on findings, highlights, and innovative solutions in DOE-sanctioned meetings, scientific conferences, and national committees. Explore the Research Support Services (RSS) contract for the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) here - https://netl.doe.gov/.

  

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

Primary Responsibilities

  • Conducting research focusing on predicting thermodynamic (e.g. enthalpy & entropy), kinetic (e.g. substitutional diffusion (including cation), interstitial diffusion), thermo-physical (coefficient of thermal expansion, thermal conductivity), defect energetics (surfaces, interfaces and grain boundaries), mechanical (e.g. elasticity, plasticity, ductility, fracture toughness, yield stress), and environmental properties (e.g. oxidation, corrosion, or hydrogen embrittlement).
  • 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.

Basic Qualifications:

  • Ph.D. degree in Physics, Chemistry, Materials Science, Chemical or Mechanical Engineering, or a related field with 1+ years experience.
  • Demonstrated proficiency in DFT calculations on diffusion, phonon, thermal conductivity and mechanical properties using VASP package.
  • 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 DFT+U calculations of oxides.
  • Experience in molecular dynamics using LAMMPS to simulate mechanical behavior of solids.
  • Experience in machine learning algorithm development.
  • 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.

Program Specific Salary Range: $85K to $105K

Pay Range:

Pay Range $74,750.00 - $115,000.00 - $155,250.00

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.

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Covid Guidance for the US

In order to enter Leidos facilities in the U.S. and to attend Leidos events outside our facilities, employees are required to be vaccinated for COVID-19 or maintain proof of a negative COVID-19 test within 96 hours of entry. In addition, we are receiving guidance from certain customers that onsite contractor personnel will need to be fully vaccinated to access customer facilities. If you are not vaccinated, please consider getting your COVID-19 vaccination as soon as possible. If you have any questions, please contact your Talent Acquisition POC. 

About Leidos

Leidos is a Fortune 500® technology, engineering, and science solutions and services leader working to solve the world’s toughest challenges in the defense, intelligence, civil, and health markets. The company’s 44,000 employees support vital missions for government and commercial customers. Headquartered in Reston, Virginia, Leidos reported annual revenues of approximately $13.7 billion for the fiscal year ended December 31, 2021. For more information, visit www.Leidos.com.

Pay and Benefits

Pay and benefits are fundamental to any career decision. That's why we craft compensation packages that reflect the importance of the work we do for our customers. Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement. More details are available here.

Securing Your Data

Beware of fake employment opportunities using Leidos’ name. Leidos will never ask you to provide payment-related information during any part of the employment application process (i.e., ask you for money), nor will Leidos ever advance money as part of the hiring process (i.e., send you a check or money order before doing any work). Further, Leidos will only communicate with you through emails that are generated by the Leidos.com automated system – never from free commercial services (e.g., Gmail, Yahoo, Hotmail) or via WhatsApp, Telegram, etc. If you received an email purporting to be from Leidos that asks for payment-related information or any other personal information (e.g., about you or your previous employer), and you are concerned about its legitimacy, please make us aware immediately by emailing us at [email protected].

If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission.

Commitment to Diversity

All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.

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