PhD student in Operation and maintenance with specialization in condition monitoring and machine learning – Luleå, Sweden

Publié il y a 2 semaines

Ref 4568-2023

Luleå University of Technology is in strong growth with world-leading competence in several research areas. We shape the future through innovative education and ground-breaking research results, and based on the Arctic region, we create global social benefit. Our scientific and artistic research and education are conducted in close collaboration with international, national and regional companies, public actors and leading universities. Luleå University of Technology has a total turnover of SEK 1.9 billion per year. We currently have 1,840 employees and 17,670 students.

In the coming years, multi-billion investments will be made in large projects in Northern Sweden to create a fossil-free society both nationally and globally. Luleå University of Technology is involved in several of these cutting-edge research projects and in the societal transformation that they entail. We offer a broad range of courses and study programmes to match the skills in demand. We hope that you will help us to build the sustainable companies and societies of the future.

Operation and maintenance is a rapidly growing research area as it is recognized as an important enabler for the business performance by industry all over the world. For many industries maintenance costs are one of the biggest individual cost items. Effective maintenance can generate income for industry through better facility utilization and higher availability. Through well planned maintenance, external and internal operational risks can also be controlled and minimized.

The division of Operation and Maintenance Engineering is multidisciplinary in nature and includes many disciplines of science and emerging technologies. The activities of the Division are aligned towards finding synergies with other engineering disciplines and building networks with many active research groups, locally and worldwide. The Division has been successful in obtaining grants from EU and Swedish Research funding agencies like VINNOVA and SSF. The Division has launched an International Journal of System Assurance Engineering and Management published by Springer. The establishment of SKF- University Technology Center for advanced condition monitoring has provided the Division with a much needed platform for the development of diagnostic and predictive technologies. At the Division two labs has been established the eMaintenance Lab and the Condition based maintenance Lab with an associated test facility for railway components. The Division is furthermore fully competent and equipped technologically to undertake research work in the emerging areas of big data, predictive and prescriptive analytics.

Subject description
Operation and Maintenance Engineering deals with the development of methodologies, models and tools to ensure high system dependability and efficient and effective maintenance processes for both new and existing systems.

Project description
This Ph.D. project aims to advance the field of railway transportation through the development of innovative condition monitoring systems for both railway vehicles and tracks. This research will leverage sensor data from various components of the railway infrastructure and apply state-of-the-art machine learning techniques and explore data-driven solutions to enhance safety, reliability, and efficiency in rail operations. The goal is to create predictive maintenance models that can proactively identify potential issues, thereby reducing downtime, minimizing maintenance costs, and ultimately improving the overall performance and sustainability of railway systems. The project will be connected to ongoing European Union research projects, where real case studies applied to railway track sections and existing vehicles will be used to validate the developed approaches. The purpose of the research is to contribute with valuable insights and generate knowledge to the field of transportation engineering by improving maintenance limits of vehicles and track infrastructure, paving the way for more sustainable and efficient railway infrastructure.

The duties within the PhD project include active participation in the research and development of deliverables connected to EU-projects. Except for the theoretical thesis work, practical work related to computer programming, simulation and on-site field measurements of different aspects will be included. Other duties related to PhD education will be specified within the individual PhD study plan.

For further information about a specific subject see; General curricula for the Board of the faculty of science and technology

In order to be eligible for employment you must have a master’s degree in the subject of computer science, technical physics, Mechatronics, Mechanical or similar education. You also have to be able to fluently speak and write in English. Topics within the degree that will be seen as meritorious are:

  • Mechanical vibrations
  • Computer programming
  • Machine learning/Artificial intelligence approaches
  • Predictive Analytics and Data Science
  • Signal processing techniques
  • Railway system engineering

Further information
Employment as a PhD student is limited to 4 years, teaching and other department duties may be added with max 20%. Placement: Luleå. Starting: upon agreement.

For more information about the position, please contact:
Matti Rantatalo, Associate Professor (+46) 920-49 2104,

Union representatives:
SACO-S Kjell Johansson, (+46 ) 920-49 1529
OFR-S Lars Frisk, (+46) 920-49 1792

In case of different interpretations of the English and Swedish versions of this announcement, the Swedish version takes precedence.

We prefer that you apply for this position by clicking on the apply button below. The application should include a CV, personal letter and copies of verified diplomas from high school and universities. Mark your application with the reference number below. Your application, including diplomas, must be written in English or Swedish.

Reference number: 4568-2023
Last day of application: 30 November 2023

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