VIE - Digital Engineer F/H

Location: [Lynchburg, VA, USA]

Department: Fuel Design

Job Type: VIE - Full-time

About the Role:

We are seeking a highly motivated AI/Digital Engineer to join our Fuel Design team in transforming how data and machine learning are applied within the nuclear fuel cycle . This role focuses on applying advanced machine learning (ML) and AI techniques to support and enhance decision-making in areas such as fuel cycle optimization, core design, inventory management, and operational forecasting .

You’ll work closely with nuclear engineers, data scientists, and software developers to build, deploy, and maintain AI-powered tools and models that solve complex business and engineering challenges.

Key Responsibilities:

• Propose, develop, and implement AI/ML models to solve real-world problems in nuclear fuel management, including:
  • Fuel loading pattern optimization
  • Burnup and depletion prediction
  • Fuel inventory planning
  • Anomaly detection in reactor operations
• Collaborate with subject matter experts to translate nuclear domain knowledge into model features and constraints.
• Design experiments and simulations using physics-informed machine learning or integrate ML with reactor simulation tools.
• Clean, preprocess, and analyze large datasets (e.g., simulation outputs, operational data).
• Build and maintain custom Gym environments or RL frameworks for nuclear fuel design and optimization.
• Communicate findings through visualizations , dashboards, and technical reports for both technical and non-technical stakeholders.
• Work cross-functionally with engineering, operations, and business units to integrate ML tools into workflows and decision systems.
• Stay current with advancements in AI/ML and evaluate their applicability in the nuclear sector.

Qualifications:

Required:

• B.S. or M.S. in Computer Science, Data Science, Nuclear Engineering, Applied Mathematics, or a related field.
• Demonstrated experience applying automation (using e.g., Python or Bash) on Linux systems to accelerate workflow and enhance data analysis.
• Strong understanding of runtime optimization and parallel computing in a HPC environment.
• Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch , or Stable-Baselines3 .
• Experience with data handling tools (e.g., NumPy , Pandas , SQL ).
  Strong understanding of supervised, unsupervised, and r einforcement learning methods.
• Familiarity with optimization algorithms , constraint handling, and evolutionary computation.
• Ability to explain technical details clearly to non-experts and collaborate across disciplines.

Preferred:

• PhD in Computer Science, Data Science, Nuclear Engineering, Applied Mathematics, or a related field.
• Knowledge of regulatory or economic constraints in nuclear fuel supply chains.

Qualifications:

Required:

• B.S. or M.S. in Computer Science, Data Science, Nuclear Engineering, Applied Mathematics, or a related field.
• Demonstrated experience applying automation (using e.g., Python or Bash) on Linux systems to accelerate workflow and enhance data analysis.
• Strong understanding of runtime optimization and parallel computing in a HPC environment.
• Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch , or Stable-Baselines3 .
• Experience with data handling tools (e.g., NumPy , Pandas , SQL ).
  Strong understanding of supervised, unsupervised, and r einforcement learning methods.
• Familiarity with optimization algorithms , constraint handling, and evolutionary computation.
• Ability to explain technical details clearly to non-experts and collaborate across disciplines.

Preferred:

• PhD in Computer Science, Data Science, Nuclear Engineering, Applied Mathematics, or a related field.
• Knowledge of regulatory or economic constraints in nuclear fuel supply chains.