Computational nuclear physics
Contact person: Ann-Cecilie Larsen
Keywords: Nuclear physics, nuclear astrophysics, nucleosynthesis, machine learning, reactor physics
Research groups: Nuclear and energy physics
Department of Physics
Recent advancements have demonstrated that machine-learning approaches are versatile and capable of solving a broad range of complex physics problems. Machine learning models can be used to help both the design and execution of experiments in nuclear physics. They can also be used to aid in the analysis of those experiments' data, of which there is often in excess of petabytes. Within this research theme, we foresee exploring fast emulators for nuclear theory, applications of machine learning within nuclear-data evaluation and predictions, detector design and accelerator controls, simulations of next-generation nuclear reactors, and improved predictions of nuclear reaction rates for large-scale simulations of heavy-element nucleosynthesis.
Research topics:
- Large scale shell model calculations
- Nucleosynthesis network calculations
- Design of nuclear-physics experiments
- Simulations of next generation nuclear reactors
- Machine learning for deconvolution of complex spectra
- Nuclear data evaluation and prediction
Mentoring and internship will be offered by a relevant external partner.