PhD Research Fellow in Science and Technology Studies (STS)

About the Department​ The Department of Physical Performance contributes through education, research, and dissemination, to an understanding of what determines physical performance with a focus on exercise physiology, biomechanics and technology. The department is responsible for teaching and conducting research within areas of how physical performance is influenced by optimal training and technique in both a sport and health perspective as well as in rehabilitation of patients.  The department's activities take place in "state of the art" laboratories, equipped for studying human motions.  The department's target groups incorporate various populations; youth, elite athletes, the elderly population and patients.  We carry specific responsibility for research within the sports of cross-country skiing, alpine skiing, swimming, orienteering, and track and field. The position and the research project​ The Department of Physical Performance at NIH offers a three- to four-year PhD fellowship from September/October 2026, four-year if teaching 25%.  The fellowship is in Exercise Physiology, with the PhD project focusing on: • biological sex differences in performance-limiting factors • durability • metabolism • fatigue development • and the influence of nutritional strategies in well-trained individuals to elite athletes in endurance sports.                       This will be achieved through a combination of laboratory- and field-based studies. The exact research questions and methodological approaches will be further refined in collaboration with the successful candidate. The PhD candidate will play an active role in all aspects of the project, including planning and development of study design, participant recruitment, data collection (assessments of performance, indirect calorimetry and gas exchanges, metabolic measurements, muscle biopsy allocation and analysis, etc.), data analysis, and dissemination of findings. The candidate will work closely with researchers at NIH and external collaborators. There will be opportunities for international collaboration, and periods of research exchange at international institutions are encouraged, although not mandatory. The project will be under the supervision of Associate Professor Øyvind Skattebo, Associate Professor Kristin Lundanes Jonvik and Professor Jostein Hallén. The successful candidate will be requested to formally apply for enrolment as a PhD student at NIH. Employment as a PhD fellow will be conditional on successful enrolment in the PhD program. Job Description​ Your main tasks as a PhD fellow at NIH are to: • Complete an independent research project under supervision. • Complete PhD courses or other equivalent educational activities corresponding to 30 ECTS points. • Actively participate in research environments, including collaboration with fellow researchers. • Dissemination of results from your PhD project, both at international conferences and in scientific journals. • Write a PhD thesis based on your project. • Optional: The candidate may gain teaching experience, depending on the department's teaching needs and the candidate's competencies. Qualification requirements and the Selection Process​ The applicant must be eligible for admission to the doctoral program at NIH: PhD program - Norwegian School of Sport Sciences. Applicants should hold a two-year master’s degree in Sport Sciences or equivalent with the grade B or better. It is the candidate responsibility to document and eventually transform the grade. Selection Criteria​ • Experience in human studies and experience in scientific writing, i.e. publications in scientific peer reviewed journals, are considered an advantage. • Demonstrate competence/experience and motivation to conduct independent research using advanced methods to investigate determinants of endurance performance such as, but not limited to, the study of metabolism and cardiorespiratory fitness using indirect calorimetry, the measurements of hemoglobin mass and blood volume using carbon monoxide rebreathing, and the analysis of metabolic markers in blood and muscle.  • Experience with performance- and cardiopulmonary exercise testing of well-trained individuals or highly trained athletes is considered a requisite • Familiarity with handling and interpreting complex datasets is an advantage. • Programming experience is advantageous but not required; willingness to learn will be considered a strength • Quality of the master's thesis and the relevance of the master's thesis to the project topic, both academically and methodologically. • Good collaboration skills, and ability to independently find innovative solutions. • Good written and oral skills in English is required. • Structured, reliable, and motivated to contribute to all phases of a research project. The purpose of the recruitment position is for the candidate to achieve a PhD during the fellowship period. Admission to the doctoral program is therefore a condition for employment as a research fellow. The candidate must be able to complete the project during the fellowship period.  The employment period is three or four years, depending on teaching qualifications and needs. The fellow will have their workplace at the Department of Physical Performance, NIH, Oslo, Norway. The candidate is required to submit an admission to the PhD program no later than six weeks after employment. A complete project description, including a proposal for the plan for the doctoral education/progress plan, will be developed in consultation with supervisors with the aim of formal admission to the doctoral program at the Norwegian School of Sport Sciences. The application will be processed in accordance with applicable guidelines for employment in fellowship positions, and employment is contingent upon admission to the PhD program. The study plan for all required coursework must be approved no later than three months after commencement. NIH offers​ • Salary as a doctoral research fellow in position code 1017 (starting salary gross annual salary NOK 550 800) • A good and active working environment at the Department of Physical Performance at NIH • Good arrangements for pension, loans, and insurance in the Norwegian Public Service Pension Fund • Excellent training facilities, corporate sports teams, and opportunities for exercise during working hours • Immediate proximity to the metro and locked bicycle parking Application should inlcude​ The application should be written in English; all attachments must be written in a Scandinavian language or English. The application with attachments must be submitted via our electronic recruitment system Jobbnorge, and comprise: • A cover letter, stating your motivation and background for applying for the specific PhD project (max. one page).  • Full CV, including a short description of relevant methodological competencies. • Diploma and transcripts of records (BSc and MSc including grade transcripts) • A list of 2-3 relevant references or recommendations, if any • A copy of the master’s Thesis  • Publication records, if any When assessing applicants, emphasis will be placed on the applicant's academic and personal qualifications to complete the project. Suitable candidates will be invited for an interview. For education outside Scandinavia, we request an explanation of the grading system. Applicants with a foreign master's degree are encouraged to apply to HKDIR for recognition of foreign education: Apply for recognition of your foreign higher education | HK-dir. Formal regulations​ For more information about the PhD program at the Norwegian School of Sport Sciences, please refer to the following website: PhD program - Norwegian School of Sport Sciences. Employment is subject to the conditions applicable to state employees. NIH has mandatory membership in the Norwegian Public Service Pension Fund. According to the Freedom of Information Act § 25, second paragraph, information about the applicant may be disclosed even if the applicant has requested not to be included on the applicant list. Inclusion and diversity are strengths, and NIH has a personnel policy goal of achieving a balanced gender composition. Furthermore, NIH seeks employees with diverse skills, subject combinations, life experiences, and perspectives. If you are a qualified applicant with a disability, a gap in your CV, or an immigrant background, we encourage you to tick the box in the job applicant portal for this. NIH will invite at least one applicant for an interview if they are qualified, and if you get the job, we will accommodate any needs you may have. The ticking is used solely for anonymized statistics, apart from selecting the right candidate. The further process​ After the deadline for applications has expired, an expert committee will be appointed in order to make an assessment of the selected applicants. Selected applicants will be called for interview, and a final decision is expected within the end of September.  Please note that the applicant will be contacted if the assessment committee requires further documentation. Contact and questions​ Interested applicants are advised to contact the academic community in preparing the application.  Further details can be obtained by contacting:  • Main Supervisor, Associate Professor Øyvind Skattebo, oyvindsk@nih.no  • Co-Supervisor, Kristin Lundanes Jonvik, kristinlj@nih.no  • Co-Supervisor, Professor Jostein Hallén, josteinh@nih.no  • Head of Department, Truls Raastad, trulsr@nih.no  Contact administration:  • HR Department, Senior Advisor Bente Johansen-Aarbostad; email: bentejoh@nih.no  • PhD Program/Study Department, Senior Advisor, Mari Corell; email: maric@nih.no

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. • Date: Wednesday 22 July 2026 • Time: 19.00-20.00 CEST • https://uio.zoom.us/j/61927781301 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​ • UiO regulations: English proficiency • UiT regulations: English proficiency 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.