PhD Fellow in Knowledge-Driven Machine Learning

The position

A position in knowledge-driven machine learning is available at the Department of Physics and Technology, Faculty of Science and Technology, within the UiT Machine Learning Group. This position is affiliated with the Center of Excellence Integreat. 

The position is for a period of four years. The nominal length of the PhD program is three years. The fourth year is distributed as 25 % each year and will consist of teaching and other duties. 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.

The project 

The objective of this position is to advance research in knowledge-driven machine learning, specifically focusing on the research themes of Integreat, at the intersection of Machine Learning, Statistics, Logics, Natural Language Processing, and structured knowledge representations. As a researcher within Integreat, you will contribute to developing next-generation Machine Learning for advanced data analysis, addressing challenges such as interpretability, learning efficiency, and reasoning. 

Potential research directions include: 

• Designing knowledge-infused learning methods that integrate external ontologies, rules, and curated databases into deep models, enhancing accuracy, interpretability, and robustness. 
• Probing and steering the internal mechanisms of deep models by aligning their representations with human-interpretable concepts and formal logical or statistical abstractions, enabling transparent, verifiable reasoning. 
• Developing principled methodologies to evaluate and improve the robustness and reliability of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. 

You will be will working within the Center of Excellence Integreat and be affiliated with the UiT Machine Learning Group, benefiting from a vibrant community of early career researchers and expert collaborators. The work will involve collaboration with the Integreat community whose members are in Tromsø and Oslo.

Your role as a PhD Fellow

You will be an integral part of Integreat via the UiT Machine Learning Group. You will engage in 
collaborative research with other members of the center and the group. Your specific research tasks 
will include:

• Conducting research in knowledge-driven machine learning.
• Developing and implementing computational models and algorithms. Analyzing results and contributing to scientific publications in peer-reviewed journals (expected 3 papers), presenting at meetings and international conferences. 
• Taking part in the teaching as teaching assistant at UiT (25% of the PhD time)
• Finally, writing up and defending a PhD thesis.

Want to know more about the position?

For further information about the position, please contact: 

• Associate Prof. Benjamin Ricaud (UiT) by email: benjamin.ricaud@uit.no 
• Professor Filippo Bianchi (UiT) by email: Filippo.m.bianchi@uit.no 

Qualifications

This position requires: 

• A Norwegian master’s degree (or equivalent) in a relevant discipline, such as: machine learning, computer science, mathematics, statistics, physics, or electrical engineering. If you are near completion of your master’s degree, you may still apply. 
• A strong formal course background in deep learning and machine learning in general, or relevant topics such as:
  • neural networks
  • self-supervised learning
  • convolutional neural networks
  • transformer-based networks
  • graph-based approaches
  • Bayesian learning
  • information theory
• Documented strong programming skills (preferably Python), for example with contributions to open-source projects, with an active Github account. 
• Documented fluency in English and ability to work in an international environment. Nordic applicants can document their English capabilities by attaching their high school diploma. 
• Fulfillment of the requirements for admission to the PhD program (next section)

Desired skills include: 

• Research experience via Master's thesis or internships involving the development of deep learning and machine learning methodology and applications
• Experience with software tools such as PyTorch, Keras, Tensorflow, and Jax
• Any relevant scientific publications
• Abilities to be creative and able to take on and develop own initiatives

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. We will also emphasize motivation and personal suitability for the position, including: 

• High self-motivation
• Ability to work independently
• Great analytical and problem-solving skills
• An excellent work ethic

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:  

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.

We offer

• An exciting research project at the forefront of knowledge-driven machine learning. 
• An internationally leading and welcoming academic environment with dedicated colleagues.
• A fantastic, lively work environment in the cosy hometown of Tromsø, surrounded by the stunning landscape of Northern Scandinavia.
• Good career opportunities  
• Flexible working hours and a state collective pay agreement  
• Pension scheme through the state pension fund 
• PhD Fellows are normally given a salary of 550 800 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: 

• An introduction and motivation letter (max 1-2 pages), including an explanation of why you are interested in this research topic and why you are the right candidate for the position. 
• CV (max 2 pages).
• 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
• 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.

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.

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.  

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.