Senior Research Fellow, Applied Artificial Intelligence

Savannah River National Laboratory•Aiken, SC

About The Position

Savannah River National Laboratory seeks an accomplished and visionary Senior Research Fellow in Applied Artificial Intelligence to report to the Associate Laboratory Director for Science, Energy and Innovation. This role will provide scientific leadership for the development and application of AI-enabled methods - including generative AI, digital twins, physics-informed machine learning, uncertainty-aware prediction, and advanced decision support - across SRNL's mission portfolio. The selected leader will help shape laboratory strategy, build a high-value applied AI portfolio, catalyze matrixed collaboration across directorates, and strengthen SRNL's position as a trusted partner for mission-critical innovation. The ideal candidate combines deep technical credibility with the ability to translate advanced analytics into operationally relevant, sponsor-valued outcomes. Why this role matters at SRNL SRNL's newly established Science, Energy and Innovation Directorate is intended to integrate fundamental research, energy resilience, and innovation under a unified framework. Applied AI can serve as a force multiplier across that portfolio when anchored in mission realities rather than treated as a stand-alone digital initiative.

Requirements

Ph.D., 10 yr+ in computer science, applied mathematics, computational science, engineering, physics, chemistry, or a closely related technical discipline.
Nationally recognized record of accomplishment in applied artificial intelligence, computational science, or AI-enabled scientific/engineering research, with visible technical contributions and external credibility.
Demonstrated success translating advanced AI/ML methods into real research, engineering, operational, or decision-support outcomes rather than purely theoretical work.
Strong understanding of scientific modeling, validation, uncertainty quantification, and the technical limitations of AI in complex physical systems.
Evidence of building externally funded programs, major partnerships, or strategically important technical initiatives in national laboratories, government R&D organizations, universities, or advanced industry.
Demonstrated ability to work effectively across disciplines and influence senior technical leaders, program managers, and executives.
Ability to obtain and maintain a DOE Q clearance.

Nice To Haves

Experience in one or more SRNL-relevant application areas such as nuclear materials, radiochemistry, tritium systems, separations, environmental monitoring, subsurface science, atmospheric modeling, advanced materials, or high-consequence industrial operations.
Experience with HPC/GPU computing environments, scientific data platforms, and secure deployment of AI tools in regulated or sensitive settings.
Experience shaping or leading matrixed, or cross-organizational technical programs.
Track record with DOE sponsors, especially EM, NNSA, Office of Science, ARPA-E, or other mission agencies relevant to SRNL growth.
Experience mentoring technical staff and helping build distinguished research communities.

Responsibilities

Develop and continuously refine SRNL's applied AI strategy and technical vision for the SEI directorate and enterprise partners, with clear alignment to mission demand, institutional capabilities, and sponsor priorities.
Define the laboratory's technical point of view on where generative AI, physics-informed machine learning, digital twins, autonomous experimentation, knowledge systems, and decision-support analytics create the greatest mission advantage.
Translate strategic intent into a sequenced portfolio of near-term wins, medium-term capability builds, and long-horizon differentiators.
Serve as senior scientific lead for mission-facing AI initiatives affecting environmental stewardship, national security, energy resilience, nuclear materials, subsurface characterization, atmospheric modeling, and related domains.
Identify high-value use cases where AI can improve throughput, quality, prediction, knowledge capture, safety margin, anomaly detection, maintenance planning, or scientific discovery.
Ensure that AI efforts are grounded in domain science and engineering, not disconnected algorithm development.
Support a matrixed operating model in which domain experts across directorates partner with a core AI capability to execute strategically selected projects.
Establish expectations for validation, uncertainty quantification, verification, reproducibility, data quality, model documentation, and human-in-the-loop decision support appropriate for mission-critical applications.
Advise laboratory leadership on technical guardrails for responsible AI deployment in high-consequence scientific and engineering environments.
Lead or sponsor peer reviews and technical risk reviews for major AI-enabled projects and serve as a visible advocate for evidence-based adoption.
Advise on the data curation, metadata, knowledge management, compute, software, and workflow architecture required to support a credible applied AI enterprise.
Partner with research computing, information technology, cyber, and line organizations to ensure infrastructure decisions serve scientific use cases, security requirements, and scaling needs.
Promote pragmatic integration of internal data resources, scientific software environments, GPU/HPC resources, and secure model deployment pathways.
Represent SRNL with DOE program offices, other national laboratories, universities, industry partners, and technical societies on matters related to applied AI and AI-enabled science and engineering.
Develop strong collaborations that position SRNL as a preferred partner for AI-enabled applied research, especially where SRNL brings unique facilities, datasets, and mission context.
Contribute to a growth strategy that converts technical credibility into sponsor confidence, multi-institution initiatives, and mission-relevant funding.
Mentor principal investigators, research staff, postdoctoral researchers, and early-career AI and domain scientists working at the intersection of mission science and advanced analytics.
Help recruit and retain high-end technical talent by making SRNL an attractive destination for applied AI researchers who want to work on consequential real-world problems.
Model a culture of collaboration, scientific integrity, disciplined execution, inclusion, and technical ambition.