The University of Stavanger (UiS) has about 12,000 students and 1,700 employees. We are the only Norwegian member of the European Consortium of Innovative Universities. The university has high ambitions. We will have an innovative and international profile, and will be a driving force in knowledge development and in the process of societal change. Together with our staff and students, we will challenge the well-known and explore the unknown.
The Department of Energy Resources is part of the Faculty of Science and Technology. The international academic staff conducts research related to energy resources, technology for improved oil recovery (IOR), decision analysis and geosciences. Study programs offer courses related to the exploration and utilization of petroleum and natural resources. The department focuses on internationalization, with development of study programs in English and high mobility among academic staff and students. The department contributes significantly to the research activities and leadership of The National IOR Centre of Norway, established by the Ministry of Petroleum and Energy. There are currently 50 employees in the department including research fellows and postdocs.
The University of Stavanger invites applications for a research fellowship on decision-driven data-analytics for the digital subsurface. The position is organizationally associated to the Faculty of Science and Technology, Department of Energy Resources.
This is a trainee position that will give promising researchers an opportunity for academic development leading to a doctoral degree.
Appointment is for three years. The position is vacant from July 2018 but there is some flexibility in the startup time.
The position is funded by DIGIRES which is a joint Research Council of Norway Petromaks-2 and industry project for the period 2018-2021, that aims to develop the next-generation digital workflows for sub-surface field development and reservoir management. As such, DIGIRES addresses new challenges in the petroleum industry related to the processing and integration of vast datasets with models for reservoir characterization and decision making.
The project has industry support from Statoil, Aker BP, ENI, VNG, DEA, Petrobras, and ENGIE. Research partners include UNI Research CIPR and the University of Bergen, and is led by professor Reidar Bratvold, Dr. Geir Evensen, and Dr. Dean Oliver. The project includes three Ph.D. students and one Post-doc, in addition to several senior researchers.
The project builds on an integrated reservoir-management philosophy for sub-surface modeling. We use multiple model realizations to characterize uncertainty together with sub-surface analytics and digitalization to handle big data. The objective of the project is to “improve decision making and uncertainty analysis for well planning and field development by using a decision-driven ensemble-based approach.” The methods developed will have applicability across a range of research areas in the Earth Sciences.
This Ph.D. position is connected to a work package on Ensemble Decision Making in DIGIRES. The work package aims to develop a process and software implementation for structuring and managing the logic required to support a decision. The logic determines how information is evaluated to arrive at a meaningful conclusion. The ensemble output from the closed-loop model management system represents the best information we can obtain of the future predictions and builds on both our theoretical knowledge and all available data combined in a statistically optimal way. Building on the experiences from the use of ensemble methods for conditioning and optimization, we will develop probabilistic decision models that will work well with an ensemble representation of the information, also when using ensembles of reasonable and computationally affordable size, e.g., O(100) realizations.
Advisors are Reidar B Bratvold and Remus Hanea in the DIGIRES project team at UiS.
- Using Dynamic Bayesian Network to encode uncertainties and dependencies of variables over time steps;
- Using Influence Diagram to identify and display how decisions, uncertainties and objectives influent each other;
- Using Approximate Dynamic Programming to identify the optimal decisions with the consideration of learning over time for sequential decision-making problems;
- Identifying appropriate Big Date and Machine Learning technologies and evaluating their applicability for solving reservoir management problems;
- Developing efficient computational algorithms to assess the a priori values of diverse types of data within the Value-of-Information framework.
- Applicants must have a strong academic background with a five-year master degree within petroleum engineering, computer science, applied mathematics, statistics or decision science, preferably recently, or possess corresponding qualifications which could provide a basis for successfully completing a doctorate.
- Both the grade for the master’s thesis and the weighted average grade of the master’s degree must individually be equivalent to or better than a B grade.
- Applicants with appropriate background and experience in Bayesian analysis, machine learning, data assimilation, inverse problems, and signal or image processing are preferable.
- The candidates should have demonstrated strong programming skills through previous studies and research.
- In evaluating the applicants, emphasis will be placed on their potential for research in the field.
The appointee must be able to work independently and as a member of a team, be creative and innovative. The research fellow must have a good command of both oral and written English. Good communication skills are necessary to improve communication between the parties in the project.
The resulting PhD degree will qualify for research and teaching positions at University level.
Candidates that are completing their master degrees in the Spring of 2018 are encouraged to apply.
The appointee will be based at the University of Stavanger, with the exception the possibility of a stay abroad at a relevant centre of research.
The research fellow is salaried according to the State Salary Code, l.pl 17.515, code 1017, of NOK 436.900 per annum. The position provides for automatic membership in the Norwegian Public Service Pension Fund, which guarantees favourable retirement benefits.
Project description and further information about the position can be obtained from Professor Reidar Bratvold, telephone 51832260, email firstname.lastname@example.org. Information about the appointment procedures can be obtained from senior HR-advisor Margot A.Treen, telephone 51831419, email email@example.com.
The University is committed to a policy of equal opportunity in its employment practices. The University currently employs few female research fellows within this academic field and women are therefore particularly encouraged to apply.
Please register your application in an electronic form on jobbnorge.no. Relevant education and experience must be registered on the form. Certificates/diplomas, references, list of publications and other documentation that you consider relevant, should be submitted as attachments to the application as separate files. The documentation must be available in either a Scandinavian language or in English. If the attachments exceed 30 MB altogether, they will have to be compressed before uploading.