Pacific Northwest National Laboratory Jobs

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Job Information

Pacific Northwest National Laboratory Tech Student - Summer Intern-Machine learning for energy storage technologies in RICHLAND, Washington

Organization and Job ID

Job ID: 311900

Directorate: Energy and Environment

Division: Energy Processes & Materials

Group: Battery Materials & Systems

Job Description

The Energy Processes & Materials Group within the Energy and Environment Directorate at Pacific Northwest National Laboratory is currently seeking summer interns to work in the area of machine learning for energy storage technology development. If you are a current graduate student ready to test your talents and training in machine learning and data science and hone your skills at a national laboratory widely recognized for its work in the computational and physical sciences, we want to connect with you. The candidate hired for this position will be responsible material/battery testing data collection, analysis, curation for large-scale material/battery performance databases. Advanced machine modeling technics such as natural language processing, deep learning, physics-informed machine learning model and advanced simulations such as density-functional theory and molecular dynamic modeling are all available. PNNL is committed to fostering a work environment that promotes inclusion, diversity, equity and accountability. We encourage all qualified applicants to apply; you do not need to meet all of the Preferred Qualifications to be considered.

What you will do:

  • Develop and apply machine learning models to support the discovery and design of materials for energy storage applications

  • Perform data analysis to understand model performance and derive insights from data sets related to material properties

  • Present research findings at technical conferences and project/program review meetings.

  • Participate in the development of manuscripts for publication in peer-reviewed scientific literature.

Minimum Qualifications

Candidates must be degree-seeking students enrolled at an accredited college or university. Candidates must be taking at least 6 credit hours and have an overall GPA of 2.5.

Preferred Qualifications

  • Experience with a range of machine learning and deep learning algorithms and approaches

  • Programming capabilities in Python and experience with Unix/Linux

  • Experience with common machine learning and deep learning libraries (e.g. Keras, PyTorch, TensorFlow, scikit-learn)

  • Knowledge of or interest in applications related to energy storage, chemistry, materials science, physics, or related domains

  • Ability to work independently and take initiative in the completion of tasks important to the project. These include preparation of first drafts of papers for peer-reviewed journals and technical presentations at scientific conferences.

  • Ability to work in collaboration with a diverse group of scientists and technical staff, and to communicate effectively.

  • Strong verbal and written communications skills.

  • Currently majoring in Chemistry, Materials Science, Chemical Engineering, Mechanical Engineering, Physics, Computer science or other related fields.

Equal Employment Opportunity

Battelle Memorial Institute (BMI) at Pacific Northwest National Laboratory (PNNL) is an Affirmative Action/Equal Opportunity Employer and supports diversity in the workplace. All employment decisions are made without regard to race, color, religion, sex, national origin, age, disability, veteran status, marital or family status, sexual orientation, gender identity, or genetic information. All BMI staff must be able to demonstrate the legal right to work in the United States. BMI is an E-Verify employer. Learn more at jobs.pnnl.gov.

If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via https://jobs.pnnl.gov/help.stm

Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from having any affiliation with the foreign government of a country DOE has identified as a “country of risk” without explicit approval by DOE and Battelle. If you are offered a position at PNNL and currently have any affiliation with the government of one of these countries you will be required to disclose this information and recuse yourself of that affiliation or receive approval from DOE and Battelle prior to your first day of employment.

Directorate: Energy & Environment

Job Category: Undergraduate Internships

Group: Battery Materials & Systems

Opening Date: 2021-04-01

Closing Date: 2021-04-22

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