Stilling:
PhD Research Fellow in “Machine Learning for Predictive Maintenance
Deadline: August 10, 2018
The Faculty of Technology, Natural Sciences and Maritime Sciences has a vacancy for a position as PhD Research Fellow in “Machine Learning for Predictive Maintenance”.
The position is attached to the Department of Electrical Engineering, Information Technology and Cybernetics (EIK) and reports to the Head of Department. The place of employment is Porsgrunn.
Qualifications
Applicants to the PhD position must have a Master’s degree or equivalent in technology or engineering with focus on modelling and with knowledge in measurement systems, sensor fusion, machine learning (neural network (ANN)/decision trees), automation and programming. It is an advantage if the applicant has experience with the challenges of collecting and handling large amount of information from measurement systems, sensor devices, IoT (Internt of Things) devices, Industry 4.0, Industrial IT, digital twins and/or deep machine learning algorithms.
It is a requirement that the successful applicant is granted admission to the university’s doctoral programme in Process, Energy and Automation Enginering.
The staff at the faculty work within subject teams to a large extent, and the candidate must be motivated to share his or her knowledge and cooperate. Personal suitability for the position will be emphasized.
Information about the position
The goal for this PhD project is to develop data driven models for predictive maintenance applied to industrial processes, mainly for the process industry. Smart buildings may also be of interest here. There are several ways for maintaining equipment in industrial processes like corrective maintenance, preventive maintenance and predictive maintenance. Predictive maintenance is important to be able to optimize the usage of time and money for maintaining a system used 24x7. In this project, models will be developed based on machine-learning algorithms. The developed models will be tested out with the current available information to predict any need for maintenance. The process of collecting the information, detecting new requirements for maintenance, and developing of models should be autonomous and robust. The work will be part of SMART research group.
The tasks will be:
- Get an overview of the state of the art for predictive maintenance in industrial applications, and propose and design a test rig for predictive maintenance,
- Propose a framework for formatting and storing the information for calibration of the model, analysis and estimation of wanted maintenance,
- Analysis of maintenance situations and evaluation of methods for detecting these situations.
- Consider data driven methods for detection, diagnosis and predictive analysis of based on the process information,
- Propose a robust solution for autonomous predictive maintenance for selected process(es),
- Test the solution(s) on a real/simulated process.
The appointment is for a term of either three years or four years with 25% teaching duties at the EIK department.
For further information concerning the position please contact:
- Associate Professor Nils-Olav Skeie (email: Nils-Olav.Skeie@usn.no / Phone: +47 35575152) or
- Professor Carlos F. Pfeiffer (e-mail: carlos.pfeiffer@usn.no / Phone: +47 35575157)
at Faculty of Technology, Natural Sciences and Maritime Sciences / Department of Electrical Engineering, Information Technology and Cybernetics (EIK).
We offer
- A professionally stimulating working environment.
- Good opportunities to develop your career and your academic skills
- A good social environment
- Attractive welfare benefits in the State Pension Plan
- Opportunity for physical activities within working hours
Salary
PhD Research Fellow (code 1017): NOK 436 900 a year. Further promotion will be based on service in the position. In special cases, employment in code 1378 may be considered. Salary NOK 436 900 – 600 200 a year. A statutory contribution will be made from the employee’s salary to the state pension plan.
Additional information
Appointment to the position will be carried out by The Appointments Board for PhD Research Fellows. An expert assessment of applicants will be carried out. Short-listed candidates will be called in for interviews and must be prepared to present and discuss their projects.
The successful applicant must comply with the laws, regulations and agreements that apply to the position.
It is an aim of personnel policy that the academic staff of University of South-Eastern Norway should reflect the composition of the general population. It is therefore a personnel policy objective to achieve a balanced age and gender composition on the faculty and to recruit people from ethnic minority backgrounds. People from ethnic minority backgrounds are encouraged to apply for the position.
There are few women employed in research positions at the Department, and consequently women are especially encouraged to apply.
According to the Norwegian Freedom of Information act § 25 2 paragraph, information about the applicant may be included in the public applicant list, even though the applicant has requested non-disclosure. The applicant will be informed if his/her request has been declined.
How to apply
University of South-Eastern Norway uses online applications. We therefore ask applicants to register their application and CV online by clicking on the “ Send application ” link to the right. The application must include the following documents:
- Certified diplomas and certificates from university college/university,
- Master’s thesis,
- A 5-page (maximum) project description on how you will implement predictive maintenance on a selected process,
- Any scientific publications and a list of these,
- Three references (contact information).
Please note that all documents must be translated into English or a Scandinavian language by an authorized translator.
Deadline: August 10, 2018
Web: www.usn.no
Kontakt: Associate Professor Nils-Olav Skeie
Telefon: +47 35575152
E-post: Nils-Olav.Skeie@usn.no
Kontakt 2: Professor Carlos F. Pfeiffer
Telefon: +47 35575157
E-post: carlos.pfeiffer@usn.no