Luleå University of Technology experiences rapid growth with world-leading expertise within several research domains. We shape the future through innovative education and groundbreaking research results and drawing on our location in the Arctic region, we create global societal benefit. Our scientific and artistic research and education are carried out in close collaboration with international, national and regional companies, public actors and leading universities. Luleå University of Technology has an annual turnover of SEK 1.9 billion. Today, we have 1,815 staff and 19,155 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 item. 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.
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.
The subject area/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.
This PhD project will deal with a part of a digital twin system where machine learning approaches and simulations of dynamic systems will be combined. The research project well be conducted within the Railway system domain. The research questions that will be addressed in this project is related to the analysis of the vehicle and rail interaction and the possibility to classify different railway systems with respect to eg. its impact on the asset degradation like rail wear and the environment or acoustic contamination aspects like curve squeal. The project will be connected to ongoing European Union research projects. Real case studies of real railway track sections will be used to validate developed approaches for railway system classification. The purpose of the research is to generate knowledge which can be used to improve maintenance limits of vehicles and track infrastructure.
The duties within the PhD project includes 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 like sound and vibrations will be included. Other duties related to the 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 interact with the Swedish authority Trafikverket and to be able to contribute to project deliverables, you should be able to fluently speak and write Swedish and English. In order to be eligible for employment you must have a civil engineer degree in the subject of computer science, technical physics, Electro, Mechatronics ore similar education. Topics within the degree that will be seen as meritorious are:
– Dynamics of mechanical systems
– Optimization theory
– Computer programming
– Machine learning/Artificial intelligence approaches
– Railway system engineering
Most employees at the subject Operation and maintenance are currently men, why we would like to see female applicants.
Fulltime fixed-term employment for four years. Departmental duties such as for example teaching might be added up to maximum 20 % of fulltime. The position is placed in Luleå, Sweden.
For further information about the position, please contact: Assoc. Prof. Matti Rantatalo, +46 920-49 2104, firstname.lastname@example.org
Union representatives: SACO-S Kjell Johansson, (+46)920-49 1529 email@example.com or OFR-S Lars Frisk, 0920-49 1792 firstname.lastname@example.org.
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: 2538-2022
Last day of application: August 15, 2022
URL to this pagehttps://www.ltu.se/ltu/Lediga-jobb?l=en&rmpage=job&rmjob=5914&rmlang=UK