PhD Fellow in Experimental Modeling of Human Error in Wind Turbine Maintenance

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  • Oslo
  • Midlertidlig
  • Fulltid
  • 17 dager siden
Oslo Metropolitan University is Norway's third largest university with 23,000 students and over 2,600 employees. We have campuses in central Oslo and at Romerike. OsloMet educates students and conducts research that contributes to the sustainability of the Norwegian welfare state and the metropolitan region.The Faculty of Technology, Art and Design (TKD) offers higher education and research and development (R&D) activities within technical subjects, arts and design. The Faculty has approximately 4.000 students and 400 staff members and is situated at Pilestredet Campus in downtown Oslo and at Kjeller Campus in Viken.Department of Mechanical, Electrical and Chemical engineering has a vacant three‑year temporary position as a PhD Fellow in Experimental Modeling of Human Error in Wind Turbine Maintenance. Do you want to contribute to a safer green transition by tackling a critical challenge in renewable energy? We are seeking a PhD fellow to investigate and reduce human error during complex wind turbine inspection and maintenance. While current industry standards focus heavily on monitoring machine health, this unique project puts the human at the center. You will combine Virtual Reality (VR) simulations, wearable physiological sensing, and machine learning to uncover how factors like fatigue and cognitive workload impact technician performance. Join us to develop predictive models that predict human error and make maintenance work safer for technicians.Your main duties and areas of responsibilityWind turbines are central to the green transition, yet their operational reliability depends heavily on effective and safe inspection and maintenance. Current maintenance approaches rely primarily on turbine sensor data, meaning that human and organizational factors are largely overlooked. This represents a significant safety gap, as human error contributes to nearly 40% of wind turbine failures and a significant share of severe accidents during maintenance activities. Such errors are frequently associated with technicians' performance shaping factors (PSFs), such as fatigue, cognitive workload, task complexity, and challenging environmental conditions.The main objective of this PhD project is to reduce human error during wind turbine maintenance by experimentally modelling these factors. Through controlled experiments using Virtual Reality (VR) simulations, combined with physiological sensors (such as fNIRS, IMUs, and heart rate monitors), the candidate will collect multimodal data on the technicians' states. Machine learning will then be used to analyze this data and develop models that can predict the probability of human error. The results will lay the scientific foundation for a human-centered approach to maintenance aligned with Industry 5.0, contributing to the design of safer and more reliable maintenance strategies.As a PhD Fellow at OsloMet, you have the opportunity to .Qualification requirements
  • Master's degree within mechanical engineering, reliability engineering, biomedical engineering, data science/machine learning, or equivalent related disciplines. The degree must normally include 120 credits (ECTS). If the master’s degree includes fewer than 120 ECTS credits, the applicant must, by the application deadline, submit documentation confirming that the master’s degree qualifies for admission to doctoral studies in the country where it was awarded. All exams for the master's degree must be completed by 30 June 2026.
  • An academic profile aligned with the needs of the research group and the department.
  • For appointment to the position, the following grade requirements normally apply:
  • Minimum average grade B on subjects included in the master's degree.
  • Minimum grade B on the master's thesis.
  • Minimum average grade C on the subjects included in the bachelor's degree. If you have an integrated master's degree, the grades from the first three standard years of the degree will be assessed.
  • Exceptions to the grade requirements are provided on the
  • Written and oral proficiency in English
Admission to the is a prerequisite for employment in the position. An application for admission to the PhD programme must be submitted within three months of the start date.General criteria for appointment to teaching and research positions are set out in the Regulations relating to the .It will be an advantage if you have
  • Strong competence in data preprocessing and feature extraction from multi modal datasets
  • Experience in developing and validating machine learning based predictive models
  • Experience with the integration and calibration of physiological sensors (e.g., fNIRS, IMUs, HRV) and the collection of datasets
  • Knowledge of safety science, human factors, or maintenance of complex systems
  • Strong collaborative skills for interdisciplinary research
Personal qualities
  • Strong ability to follow through on tasks and projects
  • Motivation for the academic and research field
  • Ability to work both collaboratively and independently
  • Strong analytical skills
  • Ability to think innovatively and contribute to development
Emphasis will be placed on personal suitability.It is important to OsloMet to reflect the population of our region, and all qualified candidates are welcome to apply. We make active endeavors to further develop OsloMet as an inclusive workplace and to make adaptations to the workplace where required. You are also welcome to apply for a position with us if you have had periods where you have not been in employment, education or training.We offer
  • Engaging work assignments within Norway's third largest university
  • Participation in research groups and a creative and inspiring academic community
  • Opportunities for academic development
  • Good welfare schemes and a wide range of sports and cultural offers
  • Loan and pension arrangements with the
  • A workplace in the center of Oslo
The salary for the position is in accordance with the Basic Collective Agreement for state employees and OsloMets pay policy for PhD Candidate in code 1017, corresponding to 550 800 NOK per year. From the salary, 2% will be deducted in pension contributions for the Norwegian Public Service Pension Fund (SPK).Application ProcessIf you wish to apply for the position, you must do so through our recruitment system.Please upload the following documents together by the application deadline:
  • Application letter describing your motivation for the position
  • CV, reference letters, and all pages of certificates/diplomas for bachelor’s and master’s degrees. Foreign education should be pre-approved, and a letter of approval must be attached. Information is available via the following link:
  • If you have not completed your master’s degree at the time of application, you must upload a provisional transcript in English or a Scandinavian language from your university by the application deadline. It is desirable that you also include an overview of the courses/exams you plan to complete during spring 2026. Relevant candidates must submit an official confirmation from the educational institution by 30 June 2026 stating that all examinations for the master’s degree have been completed, including the master’s thesis. The official diploma and transcript must be submitted before taking up the position, no later than 1 October 2026. If your educational institution does not issue the official diploma by this deadline, you must submit documentation from the institution confirming that the master’s degree has been awarded by the same deadline.
  • Master’s thesis
  • Project description/project outline (max. 5 pages including reference list)
  • Scientific works or a link to the full article, if relevant
  • A list of publications and presentations at international seminars/conferences, if relevant
  • Preferably two references with contact information
We only consider applications submitted through our electronic recruitment system, and all documents must be uploaded for your application to be considered. The documents must be in a Scandinavian language or English. Translations must be authorized, and you will be asked to present originals if you are invited for an interview. OsloMet verifies documents to give candidates a proper evaluation and ensure fair competition. Incomplete applications will not be considered.For relevant positions, OsloMet conducts background checks of applicants in cooperation with an external provider, in order to verify information contained in available documents. Background checks are always carried out with the candidate’s consent, and employment in these positions is conditional upon an approved background check. Relevant applicants will receive further information about this.Other informationIf you would like more information about the position, feel free to contact:
  • Arvind Keprate, Professor, arvind.keprate@oslomet.no, +4740386776
  • Biplab Kumar Datta, Head of Research, Bidat5779@oslomet.no, +47 99705870
  • For administrative inquiries, please contact hrtkd@oslomet.no
Deadline for application: 04.05.2026
Ref: 26/05908OsloMet holds the EU certification HR Center of Excellence for Charter & Code supporting researchers in career development, mobility and employee rights. OsloMet has signed The Declaration on Research Assessment (DORA). DORA recognizes the need to improve the ways in which the outputs of scholarly research are evaluated. The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who are seen to be in conflict with the criteria in the latter law will not be considered for the position. Visit OsloMet at LinkedIn, Facebook, Instagram Ansettelsestype: fellow | Arbeidstid: heltid | Antall ledige stillinger: 1 | Arbeidsomfang: 100% | Sted: Oslo | Fylke: Oslo | Land: Norway | Referansenummer: 26/05908 | Kontakt: Biplab Kumar Datta +4740386776 , Biplab Kumar Datta +4740386776 , | Publisert: 2026-03-26 | Søknadsfrist: 2026-05-04

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