Directorate Fellow, Energy and Data SI

Idaho National Laboratory•Idaho Falls, ID

1d•Hybrid

About The Position

Join our team here in the Nuclear Science and Technology directorate at Idaho National Laboratory to provide leading edge technical leadership to analyze data and generate results across cloud, on-premises, and edge-based computational architectures, apply technologies including explainable SI (Super Intelligence), large language models, and physics-informed machine learning, and develop software including 3D visualization tools, parallel software, and web API solutions in support of research objectives. In this role, you'll work with domain experts to understand the scope of questions data can answer, design output metrics that describe system behavior, collaborate with government agencies, academia, and industry partners, and publish results in peer-reviewed journals and conference proceedings. You will be shaping the future of how our organization builds and deploys intelligent systems.

Requirements

PhD in data science, engineering, geosciences, mathematics, computer science, physics, geology, artificial/super intelligence, or related technical discipline and 10 years of relevant experience.
Ability to interpret and communicate data-driven results within a energy domain context.
Highly skilled at managing stakeholder relationships effectively with excellence in interpersonal skills.
Highly skilled communicator across mediums and platforms.
The ability to obtain and maintain a DOE level “Q” security clearance, which requires U.S. citizenship.

Nice To Haves

15 years of relevant experience.
Experience with energy domain problems.
Expertise in complex data management.
Exposure to supervised/unsupervised learning techniques or data visualization tools.

Responsibilities

Lead SI data architectures to enable Genesis Mission science and technology challenges
Lead the development, validation, and interpretation of ML models and data pipelines in the context of energy applications.
Lead efforts in writing journal articles, conference proceedings, on energy and data AI systems
Analyze data and generate results in response to data analytics, visualization, and modeling needs on a variety of computational architectures (cloud, on-premises, edge-based).
Apply advanced technologies, such as Explainable Super Intelligence/Machine Learning (SI/ML), Large Language Models (LLMs), physics-informed machine learning, systems performance analysis, and/or data sensor fusion methodologies.
Apply energy domain knowledge to identify opportunities for machine learning and data-driven analysis within energy systems, operations, or research programs.
Collaborate effectively and consistently with data science teams to translate energy requirements into analytical frameworks.
Lead in the evaluation of model outputs using domain expertise to ensure physical accuracy and operational relevance.
Lead technical documentation, reports, and presentations related to ML-assisted efforts
Present complex information in a simplified and effective manner and provide such materials to a national and international audience.
Work effectively with little supervision on defined tasks with high complexity.