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

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 responsibility

Wind 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 apply for funding for extended research stays abroad

Qualification requirements

Admission to the PhD programme Engineering Science at the Faculty of Technology, Art and Design 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 Act relating to Universities and University Colleges

It will be an advantage if you have

Personal qualities

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

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 Process

If 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: 

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 information

If you would like more information about the position, feel free to contact:

Deadline for application: 04.05.2026Ref: 26/05908 

OsloMet 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.

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