NSIP PhD Intern - Artificial Intelligence & Data Analytics

Overview

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 AI and Data Analytics (AIDA) Division, part of the National Security Directorate, combines profound domain expertise and creative integration of advanced hardware and software to deliver computational solutions that address complex data and analytic challenges. Working in multidisciplinary teams, we connect foundational research to engineering to operations, providing the tools to innovate quickly and field results faster. Our strengths are integrated across the data analytics lifecycle, from data acquisition and management to analysis and decision support.

Read more about the AIDA division at https://(Use the "Apply for this Job" box below).-analytics

We welcome qualified individuals to express interest in this position. All candidates who meet the minimum qualifications are encouraged to apply.

Responsibilities

PNNL is seeking PhD students for assignments within the National Security Internship Program (NSIP). The AI and Data Analytics Division is looking for individuals who have a passion for solving critical national challenges using advanced computational, statistical, and mathematical techniques. The intern will be given an opportunity to be presented with complex problems in national security, energy, and science; apply cutting-edge research to make our nation safer and stronger;

develop complex computer code; develop and participate in cyber competitions; design new visualization; work with big data and optimize solutions in diverse domains.

Participants will be starting in cohort sessions and must be available to start in May or June 2026.

Diverse Focus Areas:
Your internship can be in one of six technical groups.

* Math, Stats, and Data Science:
We employ powerful tools and techniques, such as mathematical modeling and computational statistics, graph and game theory, network science, and uncertainty quantification to solve complex problems in a variety of domains.

Disciplines:
Applied Mathematics, Machine Learning, Statistics, Operations Research.

* Applied AI Systems:
We develop hardened and robust models to distill large, fast, distributed, and messy data into knowledge to support decision processes in operational environments on sponsor systems.

Disciplines:
Artificial Intelligence, Applied Machine Learning, Data Science, Deep Learning, Computer Vision,

Geospatial Intelligence, and Natural Language Processing

* Foundational Data Science:
We conceptualize and develop fundamentally new algorithms and tools to address unresolved challenges in distilling large, fast, distributed, and messy data into knowledge to support sponsors' decision processes.

Disciplines:
Artificial Intelligence, Applied Machine Learning, Data Science, Deep Learning, Computer Vision, Geospatial Intelligence, and Natural Language Processing.

* Software Engineering & Architectures:
We develop high-quality, scalable, cloud-first solutions for tackling large data pipelines and analytics that are delivered to operational sponsor environments. We use industry best practices for professional software development using Agile development practices, code reviews, automated testing, and CI/CD pipelines.

Disciplines:
Cloud Engineering, Large-Scale Data Engineering, Scalable Machine Learning/Artificial Intelligence, Dev Sec Ops , Automated Testing, Software Engineering

* Human Centered Computing:
We combine innovative interactive visualizations with advanced automated data analysis techniques to enable users to gain deeper insights from their data. Make complex data useful through skillful visual design, compelling human computer interaction, sound analytic methods, and solid engineering.

Disciplines:
Dat…