PhD Intern – Data Science

Pacific Northwest National Laboratory

7d

About The Position

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 Physical and Computational Sciences Directorate's (PCSD’s) strengths in experimental, computational, and theoretical chemistry and materials science, together with our advanced computing, applied mathematics and data science capabilities, are central to the discovery mission we embrace at PNNL. But our most important resource is our people—experts across the range of scientific disciplines who team together to take on the biggest scientific challenges of our time.
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.
PNNL is seeking a PhD intern with a focus on foundational graph models, graph representation learning, graph neural networks, knowledge graph construction and semantic search, scientific machine learning, and applications to electronic design automation and cybersecurity.

Requirements

• Candidates must be currently enrolled/matriculated in a PhD program at an accredited college.
• Minimum GPA of 3.0 is required.
• Undergraduate degree in Computer Science, Applied Mathematics, Data Science, or closely related fields.
• Background in basic and applied energy sciences (e.g., computational physics, or computational chemistry, power systems).
• 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 (e.g., Git).
• Proficiency in modern machine learning libraries (e.g., Pytorch or Tensorflow).
• Proficiency in graph databases (e.g., Neo4j).
• Experience with modern deep learning methods (e.g., graph neural networks, foundation models, large language models).
• Publication record in scientific conferences such as NeurIPS, ICML, ICLR, AAAI.

Responsibilities

• The emphasis will be given to the 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.

Benefits

• Employees are offered an employee assistance program and business travel insurance.
• Employees are eligible for the company funded pension plan and 401k savings plan, once eligibility requirements are met.

What This Job Offers

• Career Level: Intern
• Education Level: Ph.D. or professional degree
• Number of Employees: 1,001-5,000 employees