Materials Scientist 2 - Machine Learning for Materials Discovery

The Physical and Computational Sciences Directorate (PCSD) researchers lead major R&D efforts in experimental and theoretical interfacial chemistry, chemical analysis, high energy physics, interfacial catalysis, multifunctional materials, and integrated high-performance and data-intensive computing.

PCSD is PNNL’s primary steward for research supported by the Department of Energy’s Offices of Basic Energy Sciences, Advanced Scientific Computing Research, and Nuclear Physics, all within the Department of Energy's Office of Science.

Additionally, Directorate staff perform research and development for private industry and other government agencies, such as the Department of Defense and NASA. The Directorate's researchers are members of interdisciplinary teams tackling challenges of national importance that cut across all missions of the Department of Energy.

You will be part of a diverse, multi-disciplinary team committed to integrating machine learning with experimentation and simulations for materials discovery and design. Our research leverages the latest advances in artificial intelligence to navigate complex, multi-dimensional experimental and theoretical parameter spaces, aiming to create optimal materials for a sustainable clean energy future.

One of the key aspects of our work involves developing frameworks that dynamically bridge the gaps between experimental insights and theoretical models through state-of-the-art active learning techniques.

By joining our team, you will have the opportunity to collaborate with leading experts and contribute to transformative research that shapes the future of data-driven materials science at PNNL.

• Develop and deploy new machine learning capabilities for physics-guided materials design.
• Work with experimental teams to establish autonomous materials science workflows.
• Lead manuscript and support proposal development efforts.
• Present research findings at technical conferences and project review meetings.

Minimum Qualifications:

• BS/BA and 2 years of relevant experience -OR-
• MS/MA -OR-
• PhD

Preferred Qualifications:

• Ph.D. in Physics, Chemistry, Materials science, Engineering, or a related field.
• Proven background in developing machine learning solutions for physical science problems.
• Excellent Python coding skills.
• Experience with probabilistic machine learning and active learning techniques.
• Experience with participating and leading the development of research proposals.
• Excellent written and oral communication skills.