PhD Intern- Decision Intelligence at Pacific Northwest National Laboratory

Wayne State University; Richland (WA); On-site; USD 22,000 - 36,000; Full time

Job summary

Pacific Northwest National Laboratory (PNNL) invites PhD‑level interns to join the Advanced Computing, Mathematics, and Data Division (ACMDD) to explore graph neural networks and scientific machine learning with applications in electronic design automation.

The role emphasizes designing and implementing graph‑focused models, collaborating across disciplines, and communicating findings for potential publication. Strong Python, ML library experience, and a track record of research are desirable.

Qualifications

• Candidates must be currently enrolled/matriculated in a PhD program.
• Minimum GPA of 3.0 is required.
• Strong skills in graph models, graph representation learning, and ML for science.

Responsibilities

• Focus on foundational graph models, graph representation learning, and graph neural networks.
• Develop scientific software and manage scientific data following FAIR principles.
• Help summarize technical findings and contribute to peer‑reviewed publications.

Skills

Graph neural nets

Python

Machine learning

Communication

Education

PhD candidate in a relevant field

GPA 3.0 or higher

Tools

Git

PyTorch

TensorFlow

Numpy

Pandas

SciPy

Matplotlib

Job description

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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.

Responsibilities

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. The successful candidates are also expected to help summarize the technical findings and contribute to peer-reviewed publications. The successful candidates will be collaborating on a multi-disciplinary technical team and must have strong communication and interpersonal skills.

Qualifications

Minimum Qualifications:

• Candidates must be 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, SciPy, Matplotlib).


• Proficiency with software version control systems (such as Git).


• Proficiency in modern machine learning libraries (such as Pytorch or Tensorflow) is a plus


• Experience with modern deep learning methods (e.g., graph neural networks, foundation models, large language models) is a plus.


• Background in basic and applied energy sciences (e.g., computational physics, or computational chemistry, power systems) is a plus.


• Publication record in scientific conferences such as NeurIPS, ICML, ICLR, AAAI is a plus.