PhD Intern- Decision Intelligence at Pacific Northwest National Laboratory

Role

The Pacific Northwest National Laboratory (PNNL) seeks research interns with the focus on foundational graph models, graph representation learning, graph neural networks, scientific machine learning and applications to electronic design automation. The candidate should have experience with or interest in scientific software development and management of scientific data in accordance with the FAIR principles. The emphasis will be given to design and development of graph neural networks, large language models, or applications in science.

Responsibilities

• Help summarize the technical findings and contribute to peer-reviewed publications
• Collaborate on a multi-disciplinary technical team with strong communication and interpersonal skills

About PNNL

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget. Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus. The Advanced Computing, Mathematics, and Data Division (ACMDD) focuses on basic and applied computing research encompassing artificial intelligence, applied mathematics, computing technologies, and data and computational engineering. Our scientists and engineers apply end-to-end co-design principles to advance future energy‑efficient computing systems and design the next generation of algorithms to analyze, model, understand, and control the behavior of complex systems in science, energy, and national security.

Minimum Qualifications

• Currently enrolled/matriculated in a PhD program at an accredited college
• Minimum GPA of 3.0 is required

Preferred Qualifications

• Strong skills in selected areas of applied mathematics (e.g., analysis, linear algebra, machine learning, graph theory, topology, operator theory)
• Proficiency in Python language and data science packages (e.g., Numpy, Pandas, Sci Py, Matplotlib)
• Proficiency with software version control systems (such as Git)
• Proficiency in modern machine learning libraries (such as Pytorch or Tensorflow)
• Experience with modern deep learning methods (e.g., graph neural networks, foundation models, large language models)
• Background in basic and applied energy sciences (e.g., computational physics, or computational chemistry, power systems)
• Publication record in scientific conferences such as NeurIPS, ICML, ICLR, AAAI