Ledig stilling på Universitetet i Oslo

Blindern og Urbygningen (Foto: Wikimedia og Colourbox)

PhD Research Fellow in Global glacier modeling using machine learning

Deadline: 25.07.2023

Universitetet i Oslo

The University of Oslo is Norway’s oldest and highest rated institution of research and education with 28 000 students and 7000 employees. Its broad range of academic disciplines and internationally esteemed research communities make UiO an important contributor to society.

The geosciences are the studies of the planet Earth and its comparative planetology; the atmosphere, the hydrosphere and cryosphere, the Earth's surface and its interior. The Department of Geosciences conducts research and teaching in most of the domains of geoscience; geology, geophysics, physical geography, geomatics, hydrology, meteorology and oceanography. The Department is the broadest geoscience research and education environment in Norway. The Department encompasses five sections; Meteorology and Oceanography, Geography and Hydrology, Study of sedimentary basins, Environmental geosciences and Crustal Processes. We also hosts one Centre of Excellence CEED - Centre of Earth Evolution and Dynamics.

The Department aims to contribute to the new and important UN Sustainability Development Goals, and are important contributors to IPCC (UN’s Inter-governmental Panel on Climate Change). The staff consists of 40 professors and associate professors, in addition to postdoctoral fellows, PhD students, researchers, technical- and administrative staff. The Department has more than 200 employees.

Job description

A PhD Research Fellow position in Global glacier modeling using machine learning is available at the Department of Geosciences at the University of Oslo. 

No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo.

The fellowship period is 3 years with the possibility of a 4th year with compulsory other work (e.g. teaching duties at the Department of Geosciences). This is dependent upon the qualification of the applicant and the current needs of the department.

Earliest starting date is October 1, 2023 and preferably not later than 1 January 2024.

The research fellow will contribute to the development of a modeling framework suitable to reconstruct and project the multi-decadal/century evolution of glaciers (outside the ice sheets) on a global scale. The work will build on the Python Glacier Evolution Model (PyGEM) but enhance its physical basis such as the surface mass balance and/or glacier dynamics scheme while retaining computational efficiency through inclusion of machine learning techniques. The model will then be used to provide reliable policy-relevant multi-decadal/century-scale global glacier projections and scenarios.

The position is part of the recently funded EU ERC Advanced Grant “Past and Future High-resolution Global Glacier Mass Changes (GLACMASS)”. The research fellow will be part of a growing team of researchers, postdocs and PhD students working on reconstructing and projecting global glacier mass changes using machine learning and data assimilation as part of GLACMASS (see also advertised positions as Postdoctor and Researcher in the same project). Funding is available for conference attendances as well as research visits with external collaborators at universities in Europe and the USA. No fieldwork is anticipated for this project, but possibilities to gain field experience may well arise within other projects in the glaciology group.

Qualification requirements

The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials.

  • Master’s degree or equivalent in geosciences, computer/data science, physics, environmental sciences, engineering, mathematics or any other relevant field.
  • Foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system
  • The candidate must have a strong quantitative background including experience in scientific programing (using e.g., Python, Matlab, and/or Julia), and an interest in glaciology.
  • Experience in machine learning and/or bayesian statistics on spatiotemporal processes as well as modern data science tools including conda, git, docker are an asset
  • Experience in glacier, snow or climate research and handling of large data sets such as climate reanalyses and Earth observations are an advantage.
  • An excellent command of written and spoken English is required.
  • Applicants must be able to work independently while having the ability to actively communicate and co-operate within the larger research team.

Candidates without a Master’s degree must have completed their degree requirements prior to employment.

Grade requirements:

The norm is as follows:

  • the average grade point for courses included in the Bachelor’s degree must be C or better in the Norwegian educational system.
  • the average grade point for courses included in the Master’s degree must be B or better in the Norwegian educational system.
  • the Master’s thesis must have the grade B or better in the Norwegian educational system.
  • Fluent oral and written communication skills in English.
  • English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements

The purpose of the fellowship is research training leading to the successful completion of a PhD degree. The fellowship requires admission to the PhD programme at the Faculty of Mathematics and Natural Sciences. The application to the PhD programme must be submitted to the department no later than two months after taking up the position. For more information see:

We offer

  • Salary NOK 532 200 – 575 400 per year depending on qualifications as PhD Research Fellow (position code 1017)
  • Attractive welfare benefits and a generous pension agreement
  • Vibrant international academic environment
  • Career development programmes
  • Oslo’s family-friendly surroundings with their rich opportunities for culture and outdoor activities

How to apply

The application must include

  • Cover letter (including statement of motivation and research interest)
  • CV (summarizing education, positions and academic work, list of scientific publications and other relevant information)
  • Copies of the original Bachelor and Master’s degree diploma, transcripts of academic records
  • Documentation of English proficiency
  • Names of 2-3 references (name, affiliation, relation to candidate, e-mail and phone number)

The application with attachments must be delivered in our electronic recruiting system, please follow the link “Apply for this job”. Foreign applicants are advised to attach an explanation of their University's grading system. Please note that all documents should be in English (or a Scandinavian language).

When evaluating the application, emphasis will be given to the applicant’s academic and personal prerequisites to carry out the project. Applicants may be called in for an interview.

Formal regulations

Please see the guidelines and regulations for appointments to Research Fellowships at the University of Oslo.

No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo.

According to the Norwegian Freedom of Information Act (Offentleglova) information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure.

A fourth year may be considered with a workload of 25 % that may consist of teaching, supervision duties, and/or research assistance. This is dependent upon the qualification of the applicant and the current needs of the department.

The University of Oslo has an agreement for all employees, aiming to secure rights to research results etc.

Inclusion and diversity are a strength. The University of Oslo has a personnel policy objective of achieving a balanced gender composition. Furthermore, we want employees with diverse professional expertise, life experience and perspectives.

If there are qualified applicants with disabilities, employment gaps or immigrant background, we will invite at least one applicant from each of these categories to an interview.

Contact information

For further information please contact: Regine Hock, phone: +47 228 55804, e-mail: regineho@uio.no

For questions regarding Jobbnorge, please contact HR Adviser Ole Rustad, e-mail: ole.rustad@mn.uio.no

Apply for this job

Powered by Labrador CMS