ML Engineer: Predicting Critical Heat Flux (Internship)

CEA; Saclay; On-site; EUR 8,900 - 13,000; Full time

Job summary

CEA is offering a 6-month internship in Saclay to develop machine learning models for predicting Critical Heat Flux in nuclear engineering, with emphasis on uncertainty quantification and transfer learning.

The candidate should be a MSc student with strong Python skills, experience in neural networks, and familiarity with PyTorch, JAX or TensorFlow. English fluency is required; French is a plus. Start date is 01/02/2027.

Qualifications

• MSc student (M2 level) in a relevant field with strong AI background.
• Good knowledge of AI and neural networks; fluency in English.
• Experience with Python coding and code management is required.

Responsibilities

• Review literature and study previous CEA work.
• Analyse new CHF experimental database.
• Assess existing ML models on updated data.
• Develop transfer learning for CHF predictions.
• Explore explainability and uncertainty quantification methods.
• Present results and write the final report.

Skills

Python

Neural networks

Uncertainty quantification

English language

Education

Bac+5 - Diplôme École d'ingénieurs

Tools

PyTorch

JAX

TensorFlow

Job description

CEA is offering a 6-month internship in Saclay to develop machine learning models for predicting Critical Heat Flux in nuclear engineering, with emphasis on uncertainty quantification and transfer learning.

The candidate should be a MSc student with strong Python skills, experience in neural networks, and familiarity with PyTorch, JAX or TensorFlow. English fluency is required; French is a plus. Start date is 01/02/2027.