The Norwegian University of Science and Technology (NTNU) invites applications for a PhD Candidate within optimizing the operation of natural gas infrastructure in the 2021/22 academic session.

Project details:

There is a temporary PhD position available at the Department of Industrial Economics and Technology Management – Section Managerial Economics, Finance and Operation Research. The position is resident at NTNUs campus in Trondheim. This is an educational position, which will provide promising research recruits the opportunity for professional development through studies towards a PhD-degree. The position is connected to the PhD program at the Faculty of Economics and Management and the faculty will be your employer.

This project will develop models and solution methods for short-term optimization of natural gas pipeline transport and compressor management focusing on trade-offs between energy efficiency, CO2 emissions and economic values of flexibility. In addition to new models, this requires advanced stochastic optimization techniques and development of these.

As an increasing amount of natural gas is sold in short-term markets, also the shippers of natural gas have incentives to change directions of gas flows toward the hubs with the highest price. With a higher renewable share in the European energy markets, the flexibility in the natural gas system and the ability to adjust production in swing fields may in the future have a high commercial value. This is a service that can be provided both to shippers and buyers. On the other hand, there is an increased interest in reducing the CO2 footprint of oil and gas production, including reducing the compression power. This can be done using stochastic programming in more advanced compressor management, considering the optimal scheduling for security of supply and for commercial use.

Another aspect of the project is a combination of data driven optimization and machine learning to dynamically manage faults detection and responses. This is related to the model-based approach described above but has a higher focus on machine learning for prediction and links to model-based linepack optimization. Methodological challenges are:

  • Better representation of compressors in the optimization models and solution methods to handle these
  • Better representation of the linepack in dynamic models, both in multiperiod steady- state models and transient-based optimization models
  • Optimization of the tradeoffs between commercial use and security supply aspects of linepack including also energy efficiency considerations to reduce the CO2 footprint.
  •  Prediction models based on time series and/or machine learning for both events in the network (fault situations, events) and outside (nominations)

You will report to your supervisor.

Worth of Award

  • Exciting and stimulating tasks in a strong international academic environment
  • An open and inclusive work environment with dedicated colleagues
  • Favourable terms in the Norwegian Public Service Pension Fund
  • Employee benefits
  • PhD candidates are remunerated in code 1017, remunerated at gross NOK 479 600,- per annum before tax. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund.
  • The period of employment is 3 years without required teaching duties.


  • The PhD-position’s main objective is to qualify for work in research positions. The qualification requirement is that you have completed a master’s degree or second degree (equivalent to 120 credits) with a strong academic background in [subject area] or equivalent education with a grade of B or better in terms of NTNU’s grading scale. If you do not have letter grades from previous studies, you must have an equally good academic foundation. If you are unable to meet these criteria you may be considered only if you can document that you are particularly suitable for education leading to a PhD degree.
  • The position requires excellent English oral and writing skills. Some Scandinavian language skills would be preferable.

In addition, the following qualifications will contribute positively to the evaluation of the applicant:

  • Background and/or experience relevant to the project topic (see the project topic)
  • Documented experience with stochastic optimization
  • Good knowledge of digital tools like Matlab, Python, etc.
  • Documented experience with data analytics technics (e.g. machine learning, artificial intelligence)
  • Industrial experience

Personal characteristics

  • High level of personal responsibility and initiative
  • Ability to work independently as well as part of a team in accordance with the project objectives
  • Ability to work in interdisciplinary projects and teams
  • Suitable candidates should have good communication skills, be flexible and solution-oriented

How to Apply

The application must include:

  • Application letter concerning your motivation for completing a PhD
  • A short project proposal (maximum 2 pages) describing thematically problems, theories, methods and means linked to one of the topics/areas that the positions target.
  • A CV with information on education, previous research experience, together with authorized documentation of certificates and study records.
  • Academic work (not master thesis). Joint work will be evaluated. If it is difficult to identify the contributions from individuals in a joint piece of work, applicants should enclose a short descriptive summary of what she/he contributed to the work.
  • Publications and other academic works that the applicant would like to be considered in the evaluation must accompany the application.
  • Please submit your application electronically via with your CV, diplomas and certificates.

Deadline: The deadline date is November 1st 2020.


Click here for more details and to apply

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