Doktorand i härdoptimering med maskininlärning
Core
Researching advanced computational methods for nuclear reactor core and fuel cycle optimization using machine learning and optimization algorithms.
Role type
PhD researcher (Postdoc level)
Builds
Production ML models | research
Domain
Nuclear energy / Reactor physics / Machine learning
Deliverable
research
Required skills
Machine learning (neural networks, surrogate modeling), optimization algorithms, reactor physics, numerical methods, Python programming, data analysis
Preferred skills
Graph neural networks, fuel cycle analysis, uncertainty quantification, high-performance computing, version control
Technologies
Python, Julia, C++
Responsibilities
Develop and apply ML-based surrogate models for reactor physics calculations, develop and evaluate optimization methods for fuel loading patterns, analyze safety-related parameters, work with large datasets from reactor simulations, implement and document computational tools, publish research results, present at conferences
Seniority
PhD candidate (Researcher)