The position

The Department of Physics and Technology at UiT The Arctic University of Norway is pleased to announce 4 exciting PhD Fellowships within machine learning. Within the Visual Intelligence Research Centre, the PhD positions are affiliated with the UiT Machine Learning Group.

The positions are for a period of three years. The objective of the position is to complete research training to the level of a doctoral degree. Admission to the PhD programme is a prerequisite for employment, and the programme period starts on commencement of the position.

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

Place of work

Visual Intelligence (VI) is a research centre in AI funded for 8 years as a Centre for Research-based Innovation (SFI) by the Research Council of Norway (grant no. 309439) and consortium partners. In VI, you will join a strong team whose research focus is to develop next generation neural networks for advanced analysis of image and multimodal data. Central research challenges are to develop neural networks that learn more efficiently from limited data, that are better able to quantify uncertainty in predictions, that incorporate context, and that are interpretable.

The UiT campus in Tromsø is located near the city centre. Tromsø is a vibrant city located in Northern Norway, just shy of 80 000 inhabitants, surrounded by the stunning landscape of Northern Scandinavia. The location also offers ample opportunities for e.g., sighting the nothern lights, midnight sun, hiking and skiing.

The position’s field of research

As a VI researcher, you will contribute to solving the pressing societal challenges of our time for a sustainable future. You will contribute important new solutions within healthcare and precision medicine, be at the forefront in marine ecosystem monitoring by AI, enable novel methods for more efficient use of energy resources or infrastructure, and help develop better ways to observe the Earth from space to benefit the planet and to aid decision-making. This will be done by collaborating with VI consortium partners from industry and the public sector to create new innovations to benefit Norwegian value creation.

3 of the PhD positions are funded through the center's budget, and one position is funded by a UiT's interdisciplinary project called the Consortium for Patient-Centered AI (CPCAI).

For all 4 positions, potential directions are to research new ways to

Represent the general properties of relevant and real-world data by self-supervised learning towards AI foundation models.
Represent general properties of data coming from different sources, i.e. multimodal AI models (combining images, text, etc).
Understand the important mechanisms of the AI models in terms of their prototypical behaviour, individual neurons or layers within networks, or the quality of the data (data-centric intelligence).
Develop interpretable generative AI solutions.
Develop new methodology to improve the robustness and reliability of deep learning models.

Each of the 4 positions has a different innovation area:

Position 1: This position will have a medical and health innovation focus and will collaborate with one or more of the consortium’s health partners: the University Hospital of North Norway, The Cancer Registry of Norway, GE Healthcare. The candidate will develop new solutions in one or more areas such as (multimodal) MR and/or CT-based tumor segmentation and quantification, mammography-based breast cancer, or cardiac ultrasound for early detection of heart diseases.
Position 2: This position will have an energy innovation focus and will collaborate with one or more of the consortium’s energy partners: Equinor and Aker BP. The candidate will potentially work on energy foundation models based on seismic data for more efficient use of energy resources, may develop methods for characterizing the subsurface within palynology (e.g. digitized microfossil analysis), or be engaged in energy infrastructure monitoring.
Position 3: The innovation area for this position will be determined upon examination of the applicants with respect to methodological research potential or based on the consortium’s needs. Relevant innovation areas are within marine ecosystem monitoring, within medicine and health, within energy, earth observation, or a combination of use cases from all these application areas.
Position 4 (CPCAI): The innovation area of this position is within deep learning for decision and diagnosis support by analysis of data from electronic health records (EHRs). This position is conducted in collaboration with the University Hospital of North Norway and the CPCAI project. EHRs are by nature multimodal and the safe exploitation of EHRs are key for the future of the healthcare system. Potential directions include best possible multimodal representation learning in EHRs, predictions of adverse outcomes after surgery, and causal discovery (treatment-effect-counterfactuals).

Important

For all positions, transfer of methodology and collaboration across application areas are aimed for. The 4 PhD candidates will collaborate and create synergies on the core deep learning methodological research.
You must indicate any preferences you may have with respect to the 4 positions.

A detailed work plan and project description for the PhD candidate will be devised in a collaboration between the fellow, the research team and the supervisors, as well as the consortium partners.

Your role as a PhD Fellow

You will be a part of Visual Intelligence via the UiT Machine Learning Group. In addition, you will be part of the Visual Intelligence Graduate School (VIGS), a vibrant community of early career researchers within the centre. For position 4, you will in addition be part of the CPCAI project. You will engage in collaborative research with the other members of the centre and the group towards research-based innovation. You will conduct research in collaboration with VI consortium partners and must expect time and effort to go into the interdisciplinary collaboration.

You will be expected to actively collaborate with the centre’s consortium partners and interest and experience with interdisciplinary research and innovation will be considered positively. You expected to contribute to the centre’s virtual and physical seminars, to be open to collaboration across innovation areas within the centre, and to be open to collaboration between the research partners within the centre. VI hosts the conference NLDL (Northern Lights Deep Learning Conference http://nldl.org) and you are expected to be involved in the organization.

Want to know more about the positions?

For enquiries about for the position, please contact:

Qualifications

We are particularly seeking candidates with solid background in machine learning methodology, in terms of the mathematical and statistical foundation of such methods. We are seeking candidates with course work and experience in deep learning, neural networks and machine learning, e.g. self-supervised learning, convolutional neural networks, transformer-based networks, eigenvalue/eigenvector-based methods, graph-based approaches, Bayesian learning, information theory, geometric methods or neural operator learning.

Required qualifications:

You must hold a Master’s degree in machine learning or related relevant fields within e.g. mathematics, statistics, computer science, physics, electrical engineering.
A strong formal course background in deep learning and machine learning in general or relevant topics such as pattern recognition or computational statistics is required. Important topics are described above.
Documented programming skills, for example using Python, etc.
Good communication skills in English are necessary and documented fluency in English is required. Nordic applicants can document their English capabilities by attaching their high school diploma.

Preffered qualifications:

Research experience via Master thesis or internships or similar involving development of deep learning and machine learning methodology and applications.
Experience with software tools such as e.g. PyTorch, Keras, Tensorflow, and Jax.
Experience with collaborative coding, e.g. via Git/GitHub.
For position 4 (CPCAI) good oral and written command of a Scandinavian language is considered an advantage, given that free text in EHRs will be in Norwegian if working with EHR data from the University Hospital of North Norway.

Desired qualifications:

Experience in interdisciplinary and collaborative research, given that all positions aim for innovation and collaboration.
Any relevant scientific publications.
Abilities to be creative and be able to take on and develop own initiatives.

We will also emphasize motivation and personal suitability for the position. We are looking for interested, active and highly motivated candidates, who like to explore new technologies, are both independent thinking and enjoy working in a collaboration with others. We hope this is you!

In the assessment, the emphasis is on the applicant's potential to complete a research education based on the master's thesis or equivalent, and any other scientific work. In addition, other experience of significance for the completion of the doctoral programme may be given consideration.

As many people as possible should have the opportunity to undertake organized research training. If you already hold a PhD or have equivalent competence, we will not appoint you to this position.

Admission to the PhD programme

For employment in the PhD position, you must be qualified for admission to the PhD programme at the Faculty of Science and Technology and participate in organized doctoral studies within the employment period.

Admission normally requires:

A bachelor's degree of 180 ECTS and a master's degree of 120 ECTS, or an integrated master's degree of 300 ECTS.

In order to gain admission to the programme, the candidate must document sufficient potential for research. The applicant must have a grade point average of C (strong 3.0) or better for the master’s degree, which must contain an independent work. A more detailed description of admission requirements can be found here.

If you are employed in the position, you will be provisionally admitted to the PhD programme. Application for final admission must be submitted no later than two months after taking up the position.

Applicants with a foreign education will be subjected to an evaluation of whether the educational background is equal to Norwegian higher education, following national guidelines from Norwegian Directorate for Higher Education and Skills. Depending on which country the education is from, one or two additional years of university education may be required to fulfil admission requirements, e.g. a 4-year bachelor's degree and a 2-year master's degree. UiT normally accepts higher education from countries that are part of the Lisbon Recognition Convention.

Inclusion and diversity

UiT The Arctic University of 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 are 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 at 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.

We offer

An interesting research project at the forefront of deep learning research
An internationally leading and welcoming academic environment with dedicated colleagues
Good career opportunities
A large degree of independence in work
Flexible working hours and a state collective pay agreement 
Pension scheme through the state pension fund 
PhD Fellows are normally given a salary of 550 000 NOK/year with a 3% yearly increase
If you have to relocate to Tromsø then the Faculty of Science and Technology may reimburse your moving costs. Further details regarding this matter will be made available if you receive an offer from us.

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 about working and living in Norway can be found here: https://uit.no/staffmobility

How to apply

Your application must include:

Application letter (max 2 pages, detailing your motivation, explanation of why you are right for the position(s) and stating your preferences among the 4 positions)
CV
Diploma for bachelor's and master's degree
Official transcripts of grades/academic record for bachelor's and master's degree
Explanation of the grading system for foreign education (Diploma Supplement if available)
Documentation of English proficiency
Documentation of proficiency in Norwegian or a Scandinavian language (if available)
2-3 References with contact information
Master’s thesis, and any other academic works

Qualification with a master’s degree is required before commencement in the position. If you are near completion of your master’s degree, you may still apply and submit a draft version of the thesis and a statement from your supervisor or institution indicating when the degree will be obtained. You must still submit your transcript of grades for the master’s degree with your application.

All documentation to be considered must be in a Scandinavian language or English. Diplomas and transcripts must also be submitted in the original language, if not in English or Scandinavian. 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.

General information

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

The engagement is to be made in accordance with the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment.

After the appointment you must assume that there may be changes in the area of work.

Remuneration for the position of PhD Fellow is in accordance with the State salary scale code 1017. 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.

A shorter period of appointment may be decided when the PhD Fellow has already completed parts of their research training programme or when the appointment is based on a previous qualifying position PhD Fellow, research assistant, or the like in such a way that the total time used for research training amounts to three years.

UiT The Arctic University of Norway wishes to increase the proportion of female researchers. In the event that two or more applicants are found to be approximately equally qualified, female applicants will be given priority.

We process personal data given in an application or CV in accordance with the Personal Data Act (Offentleglova). According to the Personal Data Act information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure. You will receive advance notification in the event of such publication, if you have requested non-disclosure.

Assessment

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. A copy of the assessment report will be sent to all applicants.

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.

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