PhD Intern - Continuum Computing

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. 

Responsibilities

The Future Computing Technologies Group seeks a motivated PhD Intern for the Spring 2026 in novel explorations of distributed data analytics for emerging AI-assisted scientific workflows.

The successful applicant will join the Future Computing Technologies group to advance distributed data analytics for emerging AI-assisted scientific workflows. This work will focus on developing and exploring novel methods for large-scale distributed training, rapid inference and agentic services, distributed and parallel computing, and performance and workload characterization.

The role will also include modeling application power and energy usage to improve efficiency and scalability. Candidates should be familiar with topics such as distributed and continuum computing, vector databases, performance modeling, and storage or memory systems.

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:

• Currently pursuing a PhD in Computer Science, Data Science, or a related discipline.
• Familiarity with concepts such as distributed and continuum computing, vector databases, performance modeling, and storage or memory systems.