项目介绍

2026年8月20日

PhD position

There is a vacancy for a PhD position at the Geophysical Institute, University of Bergen in climate data analysis and machine learning emulation of hydrology. The position is for a fixed-term period of three years, with the possibility for a fourth year with career-promoting work (e.g. teaching at the Department), under the project DYNAMIC-AI: Dynamics of Unprecedented and Compound Extremes using AI, funded by the Bjerknes Centre for Climate Research.

About the project/work tasks

The PhD fellow will be part of the Bjerknes strategic project DYNAMIC-AI. DYNAMIC-AI will integrate physical modelling, dynamical understanding, machine learning, and hydrological modelling into a unified framework for analysing unprecedented and compound extremes. The project analyses wildfires, drought, heatwaves, strong wind, extreme precipitation and flooding. By unifying scientific approaches and BCCR expertise, we will obtain a new synthetic understanding that is more than the sum of its parts.

The successful candidate will analyse extreme precipitation events across Europe in the new generation of CMIP6 CORDEX climate downscaling ensembles, and develop a machine learning-based hydrological model to emulate the hydrological response to extreme precipitation. This model will be integrated into the full DYNAMIC-AI framework, propagating uncertainty arising from atmospheric dynamics down to hydrological hazards.

DYNAMIC-AI is an interdisciplinary project, and a collaboration between the University of Bergen, NORCE Research and the Nansen Environmental and Remote Sensing Centre (NERSC). The candidate will work in a highly international working environment as part of the Bjerknes Centre for Climate Research (BCCR) co-located with the Geophysical Institute, University of Bergen. BCCR is the largest climate research centre in the Nordic countries and among the leading centres in Europe with around 300 scientists from 40 countries.

Qualifications and personal qualities

• The applicant must hold a master’s degree or equivalent in computer science, atmospheric sciences, meteorology, hydrology, or a related field, or must have submitted their master’s thesis for assessment prior to the application deadline. It is a condition of employment that the master’s degree has been awarded.
• Experience with Python programming, the ability to work comfortably in the Unix/Linux environment, and handling large climate datasets in their native formats (e.g., NetCDF) is required. 
• Experience with at least one of the following is required: hydrological modelling; land surface processes; climate data analysis; training neural networks. Experience with multiple of them is an advantage.
• Experience with machine learning tools such as PyTorch is an advantage. 
• Understanding and/or experience in studying extreme events is an advantage.
• Experience with statistics, uncertainty quantification or uncertainty propagation is an advantage.
• Familiarity with other relevant programming languages is an advantage.
• Applicants must be able to work independently and in a structured manner and demonstrate good collaborative skills.
• Applicants must have good English skills, written and oral.

Personal and relational qualities will be emphasized. Research experience, ambitions and potential will also count when evaluating the candidates. 

Special requirements for the position 
The University of Bergen is subject to the regulations for the export control system. The regulation will be applied in the processing of the applications.

About the PhD position

About the PhD Research Fellow
The fellowship will be for a period of 3 years, with the possibility for a 4th year contingent on the qualifications of the candidate and the teaching needs of the department and will be decided by the Head of department upon appointment. If a 4th year is granted, one year of career-promoting work associated with teaching, dissemination or research infrastructure/services, will be distributed over the full employment period and thus corresponding to 25 per cent of the time each year. 

The employment period may be reduced if you have previously been employed in a qualifying post (e.g., research fellow, research assistant).

About the research training
As a PhD Research Fellow, you must participate in an approved educational programme for a PhD degree within 3 years. The deadline for applying for admission to the PhD programme at The Faculty of Mathematics and Natural Sciences is 2 months after you start your position or after the start of the research project that will lead to the PhD degree. It is a condition that you satisfy the enrolment requirements for the PhD programme at the University of Bergen.

We can offer

• a good and professionally stimulating working environment 
• salary as PhD research fellow (code 1017) in the state salary scale. This constitutes a gross annual salary of NOK 593 700. Further increases in salary are made according to length of service in the position
• enrolment in the Norwegian Public Service Pension Fund
• good welfare benefits

Your application must include

• a brief (one page max) statement of your research interests, how they pertain to the advertised position, and motivation for applying for the position
• a detailed justification of your qualifications in relation to the listed requirements, explaining how you meet each criterion. Additionally, you should address the “advantages” mentioned above, as these will be taken into account when ranking the candidates
• the names and contact information for two referees. One of these must be the main advisor for the master’s thesis or equivalent thesis
• CV
• transcripts and diplomas showing completion of the bachelor’s and master’s degrees. If you have not yet completed your master’s degree, please submit a statement from your institution confirming that the master’s thesis has been submitted
• relevant certificates/references
• approved documentation of proficiency in English (if required, cf English language requirements for PhD admission)
• a list of any works of a scientific nature (publication list)
• a copy of your master’s thesis