PhD Intern- Decision Intelligence

Pacific Northwest National Laboratory

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. The Data Sciences & Machine Intelligence group within PNNL’s Advanced Computing, Mathematics, and Data Division seeks a graduate intern to support the development of AIenabled optimization tools for critical infrastructure. The role involves extending agentic workflows to design, implement, orchestrate, and evaluate optimization formulations.

Requirements

Candidates must be currently enrolled/matriculated in a PhD program at an accredited college.
Minimum GPA of 3.0 is required.

Nice To Haves

PhD degree in Computer Science, Computer Engineering, Data Science, or a related field.
Experience with large language models or agent based AI frameworks (e.g, LangGraph, AutoGen, OptiMind).
Familiarity with optimization solvers such as Gurobi, Pyomo, or Julia/JuMP.
Understanding of multiobjective optimization, reinforcement learning, or decisionscience principles.
Coursework or project experience involving workflow systems (e.g., Snakemake, Nextflow, Airflow).
Proficiency in Python; exposure to Julia or Rust is advantageous.
Strong communication skills and the ability to contribute effectively to multidisciplinary teams.

Responsibilities

Contribute to the development of AIenabled tools for optimization and critical infrastructure analysis.
Assist with designing, coding, and testing optimization formulations under mentor guidance.
Support the creation and refinement of agentic workflows, including task orchestration and evaluation
Collaborate with researchers to gather requirements, conduct literature reviews, and analyze experimental results.
Document methods, findings, and software components to facilitate team knowledge sharing.

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.
Employees are offered an employee assistance program and business travel insurance.

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