Researchers of the HUN-REN Centre for Energy Research have long been developing atmospheric dispersion and dose calculation tools to assess radiological consequences of atmospheric releases. One of the applications of such tools is the evaluation of compliance with acceptance criteria as part of deterministic safety assessment for nuclear facilities. The calculation chain includes environmental transport modelling and dose estimation of the public around the nuclear facility.
In the last couple of years, the new calculation tool, the CARC (Calculating Atmospheric Release Criteria) code has been developed with the goal of calculating the consequences of environmental releases to confirm and determine acceptance criteria for nuclear facilities. The development of this software continues with the refinement of the atmospheric dispersion model and the methods for external and internal exposure calculations.
The PhD student will join the research group and carry out the following tasks:
- Study of the literature on atmospheric dispersion models and existing techniques of dose estimation methods.
- Investigation of potential improvements of the CARC methodology.
- Refinement of environmental transport calculation and dose assessment methods, including the integration of AI-based or data-driven components where appropriate.
- Examination of new approaches and comparison with conventional methods.
- Demonstration of the applicability of the developed system, performing calculations for confirmation of compliance with acceptance criteria.
The PhD candidate will have the opportunity to participate in international conferences in the field of atmospheric dispersion modelling, nuclear safety and dose assessments. Professional support will be provided to the PhD candidate by the Radiation Protection Department of the HUN-REN Centre for Energy Research.
The applicant should have a proper level of knowledge and experience in the field of radiation protection, dose assessment and programming. The applicant should be able to work independently, have new ideas and sufficient knowledge of English to be able to conduct review of international literature, write papers and participate in international conferences in the field. Knowledge and experience in C or C++ programming language is an advantage, and openness to learning data-driven or AI-based methods is considered beneficial.