About the positions
Interested in pursuing a PhD in machine learning in an interdisciplinary and collaborative environment in Norway?
Integreat - Norwegian Centre for Knowledge-driven Machine Learning at the University of Oslo and UiT The Arctic University of Norway invites applications for eight PhD fellowships connected to interdisciplinary projects in knowledge-driven machine learning. Four of the positions are co-funded with TRUST - The Norwegian Centre for Trustworthy AI. Integreat and TRUST provide a broader interdisciplinary research environment, offering all recruited candidates access to complementary expertise, scientific activities, and national research networks. The purpose of the fellowships is research training leading to the successful completion of a PhD degree. Successful applicants will be enrolled in the PhD programme at the University of Oslo or UiT The Arctic University of Norway.
Integreat brings together more than 100 researchers from mathematics, statistics, machine learning, logic, language technology, philosophy, and related fields. As a PhD fellow, you will become part of this interdisciplinary research community and work closely with researchers at different career stages across Integreat's partner institutions. You will also join an established cohort of early-career researchers and benefit from the centre's researcher development programme, including scientific seminars, interdisciplinary workshops, mentoring, career development activities, research mobility support, and social events.
The duration of each fellowship is specified in the individual project description. Most positions are three-year appointments. For selected projects, a four-year appointment may be offered, where the additional year is devoted to career-promoting tasks such as teaching, supervision, or other research-related duties outside the PhD project.
Starting date by agreement, depending on the project.
The place of work, either in Oslo or in Tromsø, is specified in each project description.
Working language: English
Integreat and TRUST
About Integreat
Integreat – Norwegian Centre for Knowledge-driven Machine Learning is a Centre of Excellence funded by the Research Council of Norway, with branches in Oslo at the University of Oslo and in Tromsø at UiT The Arctic University of Norway.
Machine learning is a core driver of artificial intelligence (AI) and an increasingly important force in a digital and data-driven world. Integreat develops theories, methods, models, and algorithms that combine data with general or domain-specific knowledge, helping lay the foundations for the next generation of machine learning. Integreat projects aim for more accurate, more sustainable, more explainable, and more trustworthy machine learning, with quantified uncertainty.
The centre brings together perspectives and methodologies from statistics, logic, language technology, theoretical computer science, ethics, and machine learning in new ways. Its research focuses on developing ground-breaking methods and theories for addressing fundamental challenges in science, technology, health, and society.
Integreat draws on the research strengths of researchers and students from the departments of Mathematics, Informatics, and Philosophy, the Oslo Centre for Biostatistics and Epidemiology at UiO, the Norwegian Computing Center (NR), and the machine learning group at UiT, with members from the departments of Physics and Technology, Mathematics and Statistics, and Computer Science.
About TRUST
TRUST is a Norwegian research centre dedicated to building the foundations of trustworthy AI. Its mission is to enable AI systems that are accurate, interpretable, inclusive, fair, safe, sustainable, and well-governed. By uniting expertise from (i) machine learning, statistics, mathematics and data science, (ii) law, and social sciences, and (iii) philosophy, the centre seeks to produce ground-breaking results tested on real-world problems, including healthcare, mobility, governance, security, and climate resilience.
In advancing trustworthy AI, TRUST will develop new AI technologies, support innovation, and investigate the societal consequences of AI, including the effects of AI on democracy, science, and the environment. With a consortium of over 70 partners from academia, industry, government and civil society in Norway and internationally, TRUST’s research is theoretical, methodological, legal, and empirical.
TRUST is funded by the Research Council of Norway and its public and private partners, and is led by the University of Oslo, SINTEF and the Norwegian Computing Center (NR).
Integreat and TRUST collaborate on selected research and training activities, creating opportunities for interaction across complementary research areas in machine learning and trustworthy AI. Through access to the scientific activities, expertise, and networks of both centres, recruited candidates will benefit from opportunities for scientific exchange, interdisciplinary collaboration, engagement with academic and non-academic partners, and participation in a wider national AI community.
About Integreat
See the information video for more information about Integreat.
Video:
https://www.youtube.com/shorts/-_I52cJHiW4Job description
Integreat seeks to recruit fulltime PhD fellows for eight cross-disciplinary projects spanning machine learning, statistics, logic, language technology, and ethics. The projects address fundamental challenges in modern machine learning and contribute to developing new theoretical and methodological foundations for the field.
As a PhD fellow, you will conduct independent research within one of the advertised projects under the supervision of leading researchers. You are expected to contribute actively to the research environment by participating in the centre's scientific activities, seminars, workshops, and collaborative initiatives.
Detailed information about each project, including the host department, PhD programme, starting date, and project-specific qualification requirements, is provided below and in the links.
Project descriptions
Project 1: Predictive Bayesian inference and foundation models
- Employment: University of Oslo, Department of Mathematics
- PhD programme: University of Oslo, Faculty of Mathematics and Natural Sciences
Starting date: not earlier than 2 January 2027 and preferably as soon as possible thereafter
Project 2: Bridging logic, knowledge representation and learning
- Employment: University of Oslo, Department of Informatics
- PhD programme: University of Oslo, Faculty of Mathematics and Natural Sciences
Starting date: not earlier than 2 January 2027 and preferably as soon as possible thereafter
Project 3: Data attribution for LLMs
- Employment: University of Oslo, Department of Mathematics
- PhD programme: University of Oslo, Faculty of Mathematics and Natural Sciences
Starting date: preferably in 2026, by agreement
Project 4: Measuring bias in VLMs
- Employment: University of Oslo, Department of Informatics
- PhD programme: University of Oslo, Faculty of Mathematics and Natural Sciences
Starting date: preferably in 2026, by agreement
Project 5: Models and dynamics of machine reasoning
- Employment: UiT - The Arctic University of Norway, Department of Physics and Technology
- PhD programme: UiT - The Arctic University of Norway, Faculty of Science and Technology
Starting date: preferably in 2026, by agreement
Project 6: Multi-agent knowledge bases
- Employment: UiT - The Arctic University of Norway, Department of Physics and Technology
- PhD programme: UiT - The Arctic University of Norway, Faculty of Science and Technology
Starting date: preferably in 2026, by agreement
Project 7: Probabilistic Representation Learning
- Employment: UiT - The Arctic University of Norway, Department of Physics and Technology
- PhD programme: UiT - The Arctic University of Norway, Faculty of Science and Technology
Starting date: not earlier than 2 January 2027 and preferably as soon as possible thereafter
Project 8: Structured VLMs: panoptic scene graphs for high-level reasoning
- Employment: University of Oslo, Department of Informatics
- PhD programme: University of Oslo, Faculty of Mathematics and Natural Sciences
Starting date: not earlier than 2 January 2027 and preferably as soon as possible thereafter
Online information meeting
We warmly invite prospective applicants to an online information meeting where representatives from Integreat and TRUST will introduce the research centres, present the PhD opportunities, and explain the application process. You will also have the opportunity to ask questions.
Attendance is optional but strongly encouraged.
Requirements
Qualification requirements consist of three components:
(i) general qualification requirements applicable to all applicants, (ii) admission requirements for the relevant PhD programme, and (iii) project-specific requirements.
Applicants must satisfy all applicable requirements for every project they rank.
Employment and admission to the PhD programme are conditional upon successful completion of the Master's degree and submission of official documentation confirming that the degree has been awarded. Applicants who are in the final stages of completing their Master's degree may still apply.
i) General qualification requirements
These are requirements applicable to all projects:
- Master's degree, or equivalent, in a field relevant to the ranked project
For candidates with a foreign completed degree (M.Sc.-level), it must correspond to a minimum of four years in the Norwegian educational system.
- Research interest in one or more of the research areas represented at Integreat, including machine learning, statistics, logic, language technology, ethics, mathematics, or another related field
- Fluent oral and written communication skills in English
ii) Project-specific requirements
Each project has a set of project-specific qualification requirements that are necessary for successful completion of the project. Before ranking a project, applicants must review the corresponding project description and ensure that they satisfy the project-specific requirements.
iii) PhD programme admission requirements
The purpose of the fellowships is research training leading to the successful completion of a PhD degree. Applicants must satisfy the admission requirements of the PhD programme associated with the project(s) they rank.
University of Oslo
Applicants must satisfy the admission requirements of the PhD programme at the Faculty of Mathematics and Natural Sciences, University of Oslo.
Education
- Master's degree or equivalent education relevant to the ranked project(s).
Foreign education will be assessed to determine whether it is equivalent to the relevant Norwegian degree requirements.
Grade requirements
- Bachelor's degree courses: average grade C or better
- Master's degree courses: average grade B or better
- Master's thesis: grade B or better
For applicants with foreign education, grades will be assessed in relation to the Norwegian grading scale as part of the admission process.
Further information about the UiO admission requirements
UiT The Arctic University of Norway
Applicants must satisfy the admission requirements of the PhD programme at the Faculty of Science and Technology, UiT The Arctic University of Norway.
Education
- Bachelor's degree of 180 ECTS and Master's degree of 120 ECTS, or an integrated Master's degree of 300 ECTS.
Grade requirements
- Average grade C (strong 3.0) or better for the Master's degree.
- The Master's degree must include an independent research project (e.g. Master's thesis).
Foreign education will be assessed in accordance with UiT's admission requirements.
Further information about the UiT admission requirements
English language requirements
Personal skills
We are looking for candidates who are curious, motivated to learn, and interested in tackling challenging scientific questions. You should be able to work independently while also contributing actively to collaborative research activities and the broader research community.
Integreat brings together researchers from different disciplines, institutions, and career stages. We value openness to new perspectives, effective communication, and a willingness to engage across disciplinary boundaries. We are looking for candidates who contribute positively to a supportive, inclusive, and respectful research environment.
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 (Norwegian).
The selections made in the job application portal are used for anonymized statistics that all state employers include in their annual reports.
More information about gender equality initiatives at UiO can be found here.
Integreat is committed to equity, diversity, inclusion, and belonging, guided by our INTEGREAT principles (Integrity, Non-discrimination, Tact, Environment, Gratitude, Respect, Empathy, Accountability, Transparency). We embed these values in practice through inclusive hiring and structured evaluations, targeted mentoring and career development, and flexible work arrangements and accommodations to ensure everyone can thrive and contribute.
We have a clear institutional commitment to gender equality and diversity, with dedicated initiatives and networks for women in science.
We hope you will apply for the position with us.
Evaluation and selection process
Applications will be evaluated by expert committees appointed by the participating institutions. Applicants will be assessed against the general qualification requirements, the admission requirements of the relevant PhD programme, and the project-specific requirements for each project they rank and other relevant formal national and institutional regulations.
Applicants will be evaluated separately for each ranked project based on their academic qualifications and background, research experience, motivation, potential for research, and personal suitability for the project and research environment.
Where relevant and with the applicant's consent, the evaluation committee may also consider an applicant for other projects included in the call if the applicant's qualifications and research interests are deemed to be a strong match.
Shortlisted candidates will be invited to an online interview. References may be contacted as part of the final assessment.
We offer
Research environment and career development
- A unique interdisciplinary research environment at the forefront of knowledge-driven machine learning.
- Opportunities to develop independent research questions while collaborating with leading national and international researchers.
- Structured career development, including an individual professional development plan throughout the PhD period.
- Mentoring and support tailored to early-career researchers.
- Research mobility funding supporting international collaboration and research stays.
Working environment
- A vibrant, international, and collaborative research community.
- A friendly, inclusive, and supportive working environment that values diverse perspectives and interdisciplinary exchange.
- A family-friendly and flexible workplace. We recognise that researchers have different life situations and actively support work-life balance through flexible working arrangements and consideration of caregiving responsibilities when planning conferences, research stays, and travel.
Living in Norway
- Family-friendly surroundings in Oslo and Tromsø, with excellent opportunities for culture, nature, and outdoor activities.
- A generous public pension scheme and social benefits.
- Full access to the Norwegian National Insurance Scheme and public healthcare.
- A strong institutional commitment to gender equality, diversity, and inclusion, supported by dedicated initiatives and networks for women in science.
If you have to relocate to Tromsø, 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.
Salary and employment
- Salary as a PhD Research Fellow (position code 1017) in the range NOK 550 800–595 000 per year, depending on competence and prior relevant experience. A statutory contribution of 2% to the Norwegian Public Service Pension Fund is deducted from the salary.
- For UiT positions (Projects 5–7), the starting salary is normally NOK 565 000 per year, with an annual salary increase of 3%.
How to apply
- Submit one application through the electronic recruitment portal.
- Rank at least one and up to three projects in order of preference to be considered.
- Upload all required documents in the original language together with an official translation into English or a Scandinavian language, where applicable.
Applicants with foreign education are advised to provide an official explanation of their institution's grading system.
Required documents
Name all files using this format: Surname-First name-DocumentType
- Cover letter (DocumentType: CoverLetter)
Describe your motivation, research interests, and interest in the selected project(s). - Curriculum Vitae (DocumentType: CV)
Summary of your education, employment history, academic achievements, publications, and other relevant experience. - Official transcripts of records (DocumentType: BachelorTranscript and/or MasterTranscript)
Include transcripts showing courses, credits, and grades, together with degree diplomas (DocumentType: BachelorDiploma and/or MasterDiploma) for Bachelor's and Master's degrees. - Master's thesis (DocumentType: MasterThesis)
Upload your Master's thesis and any other academic work relevant to the application (DocumentType: AcademicWork). If your thesis has not yet been completed, upload a draft version. - Project-specific documentation (DocumentType: ProjectSpecificDocumentation)
Documentation of qualifications required by the ranked project(s), where applicable. - Documentation of English proficiency (DocumentType: EnglishTest)
Upload documentation of English proficiency, where applicable. - References (DocumentType: References)
Provide the names and contact details of 2–3 references, including their relationship to the applicant, email address, and telephone number. Reference letters are not required. - Expected completion statement (DocumentType: ExpectedCompletionStatement)
Required only for applicants who have not yet completed their Master's degree. Upload a statement from your supervisor or institution confirming the expected date of completion of the degree.
Formal regulations
Successful candidates will be employed by the relevant host institution and are subject to the regulations governing employment and doctoral education in the Norwegian public sector. All qualified candidates will be assessed in accordance with applicable national export control, sanctions, and security regulations. Appointment is subject to the outcome of these mandatory checks.
Before commencement of employment, applicants must have completed their Master's degree and satisfy all relevant PhD admission requirements and be able to document that these requirements have been fulfilled, including any applicable English language requirements.
University of Oslo
Applicant lists can be published in accordance with Norwegian Freedom of Information Act § 25. When you apply for a position with us, your name will appear on the public applicant list. It is possible to request to be excluded from this list. You must justify why you want an exemption from publication and we will then decide whether we can grant your request. If we can't, you will hear from us.
Please refer to Regulations for the Act on universities and colleges chapter 3 (Norwegian), Guidelines concerning appointment to post doctoral and research posts at UiO (Norwegian) and Regulations for the degree of Philosophiae Doctor (PhD) at the University of Oslo.
The University of Oslo has a transfer agreement with all employees that is intended to secure the rights to all research results etc.
UiT The Arctic University of Norway
Further information for applicants and relevant regulations are available on UiT's website.
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 as 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 nondisclosure. You will receive advance notification in the event of such publication, if you have requested non-disclosure.
Om arbeidsgiveren:
The University of Oslo is Norway’s oldest and highest ranked educational and research institution, with 26 500 students and 7 200 employees. With its broad range of academic disciplines and internationally recognised research communities, UiO is an important contributor to society.
Integreat – Norwegian Centre for Knowledge-driven Machine Learning - Integreat is a Centre of Excellence, funded by the Research council of Norway. Integreat has two branches, one in Oslo (University of Oslo, UiO) and one in Tromsø (UiT The Arctic University of Norway). Machine learning is the mathematical and computational engine of Artificial Intelligence (AI), and therefore a fundamental force of technological progress in our increasingly digital, data- and algorithm-driven world.
Integreat develops theories, methods, models and algorithms that integrate general and domain-specific knowledge with data, laying the foundations of next generation machine learning. This will be done by combining the mathematical and computational cultures, and the methodologies and theories, of statistics, logic, language technologies, ethics and machine learning, in new and unique ways.
Focus of Integreat is to develop ground-breaking methods and theories, and by this solving fundamental problems in science, technology, health and society. Integreat draws on the research strengths of researchers and students from the departments of Mathematics, Informatics, Philosophy, and the Oslo Centre for Biostatistics and Epidemiology at UiO, the Norwegian Computing Centre (NR) and the ML group at UiT, with members from the departments of Physics and Technology, Mathematics and Statistics, and Computer Science.
