Contact person: Ann-Cecilie Larsen
Keywords: Nuclear physics, nuclear astrophysics, reactor physics, nuclear medicine, machine learning,
Research group: Nuclear and energy physics
Department of Physics, Norwegian Nuclear Research Centre
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, detector design and accelerator controls, simulations of next-generation nuclear reactors, simulations of radiation treatment of cancer, as well as for improved predictions of nuclear reaction rates for large-scale simulations of heavy-element nucleosynthesis.
Relevant topics:
- Large scale shell model calculations
- Nucleosynthesis network calculations
- Simulations of next generation nuclear reactors
- Machine learning for deconvolution of complex spectra
- Nuclear data evaluation
- Simulation of radiation treatment of cancer
Mentoring and internship will be offered by a relevant external partner.