School of Engineering Sciences at KTH

Job description

We are looking for a PostDoc to apply machine learning methods (ML) to modeling of severe accident phenomena in nuclear power plants and risk analyses. Designs of currently operating plants, modern small modular reactors and liquid metal cooled reactors will be of interest. The ML tools will be developed and trained to predict evolution of the accident progression in time with high computational efficiency. The trained ML models will be used to solve such problems as (i) assessment of the risk of releases of radioactive products to environment, (ii) development of severe accident simulators and operator support systems to minimize the consequences of the accidents. The work will be a part of national and international collaborative projects. You will be a part of research team with several PhD students and senior researchers.

What we offer

  • A position at a leading technical university that generates knowledge and skills for a sustainable future
  • Engaged and ambitious colleagues along with a creative, international and dynamic working environment
  • Work in Stockholm, in close proximity to nature
  • Help to relocate and be settled in Sweden and at KTH
  • Challenging tasks at the scientific frontiers of machine learning methods, nuclear power technology and safety.
  • Opportunity to expand your professional network, to participate in the international conferences and project meetings, to gain experience in co-supervising MSc and PhD students and in writing research project proposals.

Read more about what it is like to work at KTH



  • A doctoral degree or an equivalent foreign degree, This eligibility requirement must be met no later than the time the employment decision is made.
  • Demonstrated scientific excellence in the form of publication in research journals
  • Furhtermore, you show a great ability to collaborate within a research team
  • You should also have an independence in planning and execution of research

Preferred qualifications

  • A doctoral degree or an equivalent foreign degree, obtained within the last three years prior to the application deadline
  • Expertise in severe accident phenomena and / or development and application of machine learning methods to modeling of mechnistic systems.
  • Experience with risk analysis, Risk Oriented Accendent Analysis Methodology (ROAAM)
  • Experience with severe accident and containment thermal hydraulics codes susch as MELCOR, ASTEC, MAAP, GOTHIC
  • Teaching abilities
  • Awareness of diversity and equal opportunity issues, with specific focus on gender equality

Great emphasis will be placed on personal competency.

Trade union representatives

You will find contact information to trade union representatives at KTH's webbpage.


Log into KTH's recruitment system in order to apply to this position. You are the main responsible to ensure that your application is complete according to the ad.

Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).

About the employment

The position offered is for, at the most, two years.

A position as a postdoctoral fellow is a time-limited qualified appointment focusing mainly on research, intended as a first career step after a dissertation.


Striving towards gender equality, diversity and equal conditions is both a question of quality for KTH and a given part of our values.

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Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.


Type of employment Temporary position
Contract type Full time
First day of employment As soon as possible or according to agreement
Salary Monthly salary
Number of positions 1
Full-time equivalent 100%
City Stockholm
County Stockholms län
Country Sweden
Reference number S-2023-0411
  • Pavel Kudinov, Head of Nuclear Engineering,
  • Professor Pär Olsson, Head of department of physic,
Published 02.Mar.2023
Last application date 10.Jul.2023 11:59 PM CEST

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