About this role
About the position Position as PhD Research Fellow in Active Learning for Arctic observing systems available at the Department of Geosciences Starting date is as soon as possible. The fellowship period is 3 years. 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. No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo. Job description The PhD fellow will contribute to the development of an observing system for land-atmosphere fluxes of carbon, water, and energy in arctic environments. Observations from eddy flux towers, drones carrying meteorological sensors and gas analyzers, soil sensors, and satellite imagery are fused with land-surface models using data assimilation. The goal is to develop an adaptive experimental design frame-work for the observing system to guide ongoing measurement campaigns and targeted, computationally expensive, model simulations. This experimental design process is envisioned to update iteratively as new data become available to optimally infer surface fluxes across the landscape. The work will build on and extend the existing infrastructure at the Department of Geosciences, including mobile flux towers and drone-based observing systems developed in-house. Fieldwork for testing newly developed algorithms is anticipated in mainland Norway, Svalbard, and abroad. Funding is also available for conference attendances and research visits with external collaborators. The position is part of the ERC-funded project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD fellow will be part of a growing team of researchers, postdocs and PhD students working on intelligent observing systems using machine learning and data assimilation methods in the ACTIVATE project. What skills are important in this role? Qualifications 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. Required qualifications: Master’s degree or equivalent in geosciences, mathematics, comput-er/data science, physics, environmental sciences, 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, R, or Julia) A strong interest in land-atmosphere interactions, data assimilation methods, and land-surface modelling is required. The ability to meet the requirement for security clearance is required Desired qualifications: Experience with data assimilation, probabilistic machine learning, Bayesian inference, inverse modeling, and/or simulation-based inference is an advantage. Experience with land-surface models, micro-meteorology, Earth system modeling, and/or satellite remote sensing is an advantage. Experience with fieldwork in challenging environments is an advantage . Language requirement: Good oral and written communication skills in English English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements: https://www.mn.uio.no/english/research/phd/regulations/regula-tions.html#toc 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 The purpose of the fellowship is research training leading to the successful com-pletion of a PhD degree. For more information see: Supplementary Regulations at the Faculty of Mathematics and Natural Sciences to the Regulations for the Degree of Philosophiae Doctor (PhD) at the University of Oslo - The Faculty of Mathematics and Natural Sciences. All candidates and projects will have to undergo a check versus national export, sanctions and security regulations. Candidates may be excluded based on these checks. Primary checkpoints are the Export Control regulation, the Sanctions reg-ulation, and the national security regulation. What are we looking for in you? Personal skills: Applicants must be able to work independently while having the ability to actively communicate and co-operate within the larger research team. We need different perspectives in our work UiO is an open and internationally oriented comprehensive university that strives to be an inclusive and diverse workplace and academic environment. You can read more about UiO’s work on equality, inclusion, and diversity at uio.no . We fulfill our mission most effectively when we draw upon our variety of experiences, backgrounds, and perspectives. We are looking for great colleagues—could you be the next one? We will do our best to accommodate your needs. Relevant adjustments may include modifications to working hours, task adaptations, digital, technical, or physical adjustments, or other practical measures. If you have an immigrant background, a disability, or CV gaps, we encourage you to indicate this in the job application portal. We always invite at least one qualified candidate from each group for an interview. In this context, disability is defined as an applicant who identifies as having a disability that requires workplace or employment-related accommodations. For more details about the requirements, please refer to the Employer portal