A Postdoctoral Research Fellow position is available at the Department of Mathematics and Statistics, Faculty of Science and Technology in machine learning for graphs and time series data.

The position is a fixed term position for a duration of 2,5 years. Appointment to the position of Postdoctoral Research Fellow is mainly intended to provide qualification for work in top academic positions. It is a prerequisite that the applicant can carry out the project over the full course of the employment period. No person may hold more than one fixed term position as a Postdoctoral Research Fellow at the same institution.

The workplace is at UiT in Tromsø. The candidate must be able to start in the position in Tromsø within a reasonable time, within 6 months after receiving the offer.

If you receive a personal overseas research grant from NFR it is possible to apply NFR for an extension of the fellowship period corresponding to the length of the stay abroad (minimum three months, maximum 12 months).

The research will be framed within the activities of ARC - Arctic Centre for Sustainable Energy and will be carried out in collaboration with three industrial partners in Northern Norway (Ishavkraft, Finnmarkkfrat, ELMEA) and the following universities:

The research will mainly focus on basic research in machine learning models for time series and graphs. One of the main goal will be to push the boundaries in the field of relational deep learning by:

The goal is to generate informative spatio-temporal representations without the need for traditional training or supervision. By using suitable randomized techniques, it will be possible to improve the scalability of large spatio-temporal models without compromising their performance.

The objective is to create multi-scale representations with graph pooling (a procedure to generate smaller graphs that carry the original information) to manage the complexity of spatio-temporal models. New graph pooling techniques suitable for spatio-temporal data will be developed and used to enhance the performance on tasks of interest (e.g., forecasting), to identify underlying factors in the system, handle missing data, and integrate multi-resolution data from various sources.

The goal is to model the uncertainty in deep learning models for spatio-temporal data by means of Bayesian and frequentist approaches. This involves modifying existing deterministic models to include probabilistic components and extending techniques for generating confidence intervals to spatio-temporal data by addressing the challenge of capturing both spatial and temporal dependencies.

Develop new techniques that allow for a human-understandable explanation of the model’s output, thus aiding in systematic pattern discovery within the data. Due to the complex and irregular structure of spatio-temporal data, existing interpretability tools are not suitable. The goal is to extend current approaches to spatio-temporal models and to develop new ones based on probabilistic frameworks.

The methodologies developed in the project will be mainly applied to analyze energy systems. These systems present complexities that traditional models frequently struggle to address, and the application of advanced relational deep learning techniques aims to provide more effective solutions. The main applications in energy analytics will be: enhanced load forecasting, dynamic power flow optimization, and localization of energy faults on the grid.

While energy analytics will be the main field of application, since the methodologies in work packages 1-4 are general purpose, other application areas can also be considered.

For further information about the position, please contact Associate Professor Filippo Maria Bianchi: email: filippo.m.bianchi@uit.no

For more information about the project and the partners, please refer to the project website: https://en.uit.no/project/relay

This position requires a Norwegian doctoral degree (PhD) or an equivalent foreign doctoral degree in Computer Science, Mathematics, or Physics with a dissertation focused on machine learning, deep learning, or a directly related area.

The applicant must possess:

The following competencies will be positively evaluated:

Fluency in English is required. Nordic applicants can document their capabilities by attaching their high school diploma.

In the assessment the main emphasis will be attached to the submitted works and the project proposal for the qualifying work. Emphasis shall also be attached to experience from popularization/dissemination and academic policy and administrative activity.

During the assessment emphasis will be put on the candidate’s motivation, potential for research, and personal suitability for the position. We are looking for candidates who:

At UiT we put emphasis on the quality, relevance and significance of the research work and not on where the work is published, in accordance with the principles of The San Francisco Declaration on Research Assessment (DORA).

UiT The Arctic University i Norway is working actively to promote equality, gender balance and diversity among employees and students, and to create an inclusive and safe working environment. We believe that inclusion and diversity is a strength, and we want employees with different competencies, professional experience, life experience and perspectives.  

If you have a disability, a gap in your CV or immigrant background, we encourage you to tick the box for this in your application. If there are qualified applicants, we invite least one in each group for an interview. If you get the job, we will adapt the working conditions if you need it. Apart from selecting the right candidates, we will only use the information for anonymous statistics. 

Norwegian health policy aims to ensure that everyone, irrespective of their personal finances and where they live, has access to good health and care services of equal standard. As an employee you will become member of the National Insurance Scheme which also include health care services.

More practical information for working and living in Norway can be found here: https://uit.no/staffmobility 

Your application must include:

If you're in the final stages of your PhD, you may still apply for the position, provided that you submit parts of your dissertation along with your application. This enables the evaluation committee to assess the quality and likelihood of completion by the desired employment date. You must include a statement from your supervisor or institution stating the expected completion date for your PhD degree. Documentation of your completed PhD degree must be submitted before commencement.

All documentation to be considered must be in a Scandinavian language or English. If English proficiency is not documented in the application, it must be documented before starting in the position. We only accept applications and documentation sent via Jobbnorge within the application deadline.

The applicants will be assessed by an expert committee. The committee's mandate is to undertake an assessment of the applicants' qualifications based on the written material presented by the applicants, and the detailed description draw up for the position.

The applicants who are assessed as best qualified will be called to an interview. The interview should among other things, aim to clarify the applicant’s motivation and personal suitability for the position. A trial lecture may also be held.

The appointment is made in accordance with State regulations and guidelines at UiT. At our website, you will find more information for applicants. 

The remuneration for Postdoctoral research fellow is in accordance with the State salary scale code 1352. A compulsory contribution of 2 % to the Norwegian Public Service Pension Fund will be deducted. You will become a member of the Norwegian Public Service Pension Fund, which gives you many benefits in addition to a lifelong pension: You may be entitled to financial support if you become ill or disabled, your family may be entitled to financial support when you die, you become insured against occupational injury or occupational disease, and you can get good terms on a mortgage. Read more about your employee benefits at: spk.no.

The successful candidate must be willing to get involved in the ongoing development of their department and the university as a whole.

According to the Norwegian Freedom and 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. 

UiT The Arctic University of Norway is a multi-campus comprehensive university at the international forefront. Our vision is to be a driving force for developing the High North. The Northern Sami notion eallju, which means eagerness to work, sets the tone for this motive power at UiT. Along with students, staff and the wider community, we aim to utilise our location in Northern Norway and Sápmi, our broad and diverse research and study portfolio and interdisciplinary advantage to shape the future.

Our social mission is to provide research-based education of high quality, perform artistic development and carry out research of the highest international quality standards in the entire range from basic to applied. We will convey knowledge about disciplines and contribute to innovation. Our social mission unites UiT across various studies, research fields and large geographical distances. This demands good cooperation with trade and industry and civil society as well as with international partners. We will strengthen knowledge-based and sustainable development at a regional, national and international level.

Academic freedom and scientific and ethical principles form the basis for all UiT’s activities. Participation, co-determination, transparency and good processes will provide the decision-making basis we need to make wise and far-sighted priorities. Our students and staff will have the opportunity to develop their abilities and potential. Founded on academic integrity, we will be courageous, committed and generous in close contact with disciplines, people and contemporary developments.

We will demonstrate adaptability and seek good and purposeful utilisation of resources, so we are ready to meet the expectations and opportunities of the future. We will strengthen the quality and impact of our disciplines and core tasks through the following three strategic priority areas.