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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.
The subject area of Operation and Maintenance Engineering is multidisciplinary in nature, transcending the boundaries and separating many disciplines of science, emerging technology and arts. 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 predictive technology. Besides, two eMaintenance Labs are functioning at Luleå University of Technology and LKAB, Kiruna; a Condition Monitoring Lab has been established at the Division. The Division is 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 project is conducted in close collaboration with the Swedish mining company LKAB. In this position, you will mainly be working on one of our research projects called ‘AI Factory /PHM’, which focuses on research related to Industrial AI, eMaintenance and ‘Prognonostics and Health Management (PHM)’ in mining industry, including Machine Learning, Transferred Learning, and Deep Learning. The project aims to facilitate the decision-making in operation and maintenance by developing and demonstrating solutions based on the Digital Twin concept, empowered by AI and digital technologies.
This project will contribute to increased utilization of AI and digitalisation of the mining industry, by conducting research within:
- Industrial AI
- Digital Twin
- Nowcasting and forecasting
- Machine Learning
- Deep Learning
- Business Intelligence
- Big Data
- Cloud/edge Computing
- Information Logistics
- Operation & maintenance
- eMaintenance
The project will be carried out in close collaboration with representatives from the construction industry. The work will be carried out in a project form consisting of doctoral students, senior researchers and industry representatives. The project includes travel within and outside Sweden.
Duties
You will be working in the research team of Industrial AI and eMaintenance. In this position you will also contribute to further development of our platform ‘AI Factory’ and enhance the capabilities in our lab ‘eMaintenance LAB’.
The work will include:
- Studies of relevant theoretical frameworks.
- Mapping needs and requirements from an industrial perspective.
- Identify and analyse gaps in industrial and academic contexts.
- Design of solutions, ink. methodologies, technologies, and tools.
- Development of AI algorithms, tools, and solutions using methods including but not limited to mathematical programming, metaheuristics, robust optimization, stochastic optimization.
- Publication in academic journals and conferences.
- Participating as a lecturer and assistant in the Division’s courses.
Qualifications
- Applicants must have an MSc degree from maintenance and operation engineering, computer science, applied physics, control technology, signal processing, or equivalent.
- Applicants should have good knowledge of modeling and software development.
- Mining experience is meritorious.
- In order to communicate within the projects and with different stakeholders, we require you to master Swedish, in speeches and in writing, and have good knowledge of speech and writing in English
- Experience in the mining industry as well as knowledge in the maintenance area and software development are meritorious.
- Experience of Azure environment, platform and Azure AI services is meritorious.
- A background and experience in building mathematical models, optimization methods, simulation techniques, but also interest in metaheuristics, statistics, and machine learning.
- The candidate should be proficient in programming languages such as Python, R, MATLAB, and their associated simulation and optimization libraries and packages.
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: by agreement.
For further information about the position, please contact Professor Ramin Karim, (+46)920-49 2344, ramin.karim@ltu.se
Union representatives:SACO-S Kjell Johansson (+46)920-49 1529 kjell.johansson@ltu.se, OFR-S Lars Frisk, (+46)920-49 1792 lars.frisk@ltu.se
In case of different interpretations of the English and Swedish versions of this announcement, the Swedish version takes precedence.
Application
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.
Closing date for applications: April 17, 2023
Reference number: 996-2023