PhD Position in Risk Analysis and Risk Management for Autonomous Passenger Ferries
The Norwegian University of Science and Technology (NTNU) is now launching a strategic research program called Digital transformation with nine research projects. One of these projects is about the development of autonomous all-electric passenger ferries for urban water transport (Autoferry). Succeeding with the Autoferry project requires a broad interdisciplinary approach and involves six PhD positions. The position announced here is on the topic of risk analysis and risk management, and is affiliated with the Department of Marine Technology at the Faculty of Engineering. The place of work is in Trondheim.
This is a researcher training position aimed at providing promising researcher recruits the opportunity of academic development in the form of a doctoral degree.
Decisions about risk depend on models describing how accidents occur and data used in these models. Both models and data are uncertain, creating uncertainty about whether an accident will occur or not. To protect ourselves against negative, but uncertain, outcomes, we spend significant resources on reducing risk by implementing technical, operational and organizational barriers.
Digitalization provides opportunities for changing this situation:
- New sensors give access to data that enables development and use of more detailed risk models for decision support.
- Opportunities for continuous updating of the risk estimates.
- The models can further be trained using machine learning techniques, enabling detection of new causal relationship and improving our knowledge of the strength of the relationships.
The effect of this is that we can radically change the way risk management is performed, managing risk by reducing uncertainty rather than just analyzing uncertainty. We can then achieve:
- Risk reduction, by more accurately identifying safety critical conditions and states before accidents occur.
- Cost reduction and simplification, because technical barriers can be simplified with more accurate predictions of future conditions and states.
- Efficiency improvement through removal of organizational and procedural barriers that are time-consuming today.
The PhD candidate will work with the following topics:
- Analysis of integrity of data from sensor systems and potential impact on risk modelling.
- Exploring opportunities for developing risk models that are dynamically modified based on collected data and machine learning methods.
- Exploring implications of improved risk analysis for risk management of autonomous ships
The PhD work will be performed in collaboration with other researchers at NTNU and with UCLA, Los Angeles. The PhD student will work under supervision of professors from the Department of Marine Technology and the Department of Mechanical and Industrial Engineering. Working in a cross-disciplinary and innovative research project is a great opportunity for those who are passionate about learning!
We seek a highly-motivated individual holding a master’s degree, preferably in risk/reliability although other backgrounds may also be relevant and will be considered. This includes marine/ocean engineering, cybernetics and computer science with supplemental background in risk/reliability. The average grade must be B or better. Publication activities will be considered an advantage. Motivation for fundamental scientific research of practical relevance is essential. The applicant should have good communication skills in English and be willing to collaborate with other researchers in the cross-disciplinary group of people connected to this project.
See here for regulations concerning appointment and promotion to teaching and research posts: https://www.regjeringen.no/no/dokumenter/regulations-concerning-appointment-and-promotion-to-teaching-and-research-posts/id2519258/
PhD Candidates are remunerated in code 1017, and are normally remunerated at gross NOK 436 900 before tax. There will be a 2 % deduction to the Norwegian Public Service Pension Fund from gross wage.
Engagement as a PhD Candidate is done in accordance with “Regulation concerning terms and conditions of employment for the posts of post-doctoral research fellow, research fellow, research assistant and resident”, given by the Ministry of Education and Research of 19.07.2010. The goal of the positions is to obtain a PhD degree. Applicants will engage in an organized PhD training program, and appointment requires approval of the applicants plan for a PhD study within three months from the date of commencement.
The appointment is for a term of 3 years without teaching assistance, or up to 4 years including 25% of teaching assistance.
For further information about the position, please contact Professor Stein Haugen, Department of Marine Technology, NTNU,Trondheim. Email: firstname.lastname@example.org.
See https://www.ntnu.edu/iv/doctoral-programme for more information.
The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants. The positions adhere to the Norwegian Government's policy of balanced ethnicity, age and gender. Women are encouraged to apply.
The application must contain information of educational background and work experience. Certified copies of transcripts and reference letters should be enclosed. Applications with CV, grade transcripts and other enclosures should be submitted via this webpage at www.jobbnorge.no.
Mark the application with SO IV-151/18
Application deadline is May 27th, 2018
According to the new Freedom of Information Act, information concerning the applicant may be made public even if the applicant has requested not to be included in the list of applicants.
About this job
- Deadline Sunday, May 27, 2018
- Employer NTNU - Norwegian University of Science and Technology
- Municipality Trondheim
- Place of service Department of Marine Technology
- Jobbnorge ID 151489
- Internal ID 2018/11587
- Scope Fulltime
- Duration Temporary
- Applications on this job are registered in an electronic form on jobbnorge.no
- You must complete: Standard CV
- Please refer to where you first saw this job advertised!