PhD Research Fellow in Intelligent Dynamic Energy Systems available at the Department of Technology Systems
Three positions as PhD Research Fellow in Intelligent Dynamic Energy Systems available at the Department of Technology Systems (ITS) in the following areas:
- Numerical modeling of Dynamic Energy Systems
- Machine Learning applied to Forecasting of Energy Production and Consumption in Intelligent Dynamic Energy Systems
- Control of Intelligent Dynamic Energy Systems with Machine Learning.
The workplace is at ITS, Kjeller which is located 20 km north east of Oslo.
No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo. Starting date, as soon as possible.
The fellowship period is 3 years and devoted to carrying out a project entitled IDES.Foto: Colourbox
Job description
The PhD research fellow will be part of the IDES (Intelligent Dynamic Energy Systems) project. IDES is a collaboration project between the Institute For Energy Technology (IFE), The Norwegian Defense Research Establishment (FFI) and Department of Technology Systems (ITS) at the University of Oslo, all located at Kjeller outside Oslo, Norway.
Energy systems can be complex with many components exhibiting highly dynamic behavior in terms of unpredictability in energy consumption and production. For example, variable renewable energy sources, such as solar power or wind power, are intermittent and can be hard to predict, so dealing with the dynamics in energy systems is a challenge that is expected to receive growing interest in the years to come. The project will employ three PhD fellows in total:
- One PhD candidate (PhD 1) will develop a generic numerical model for dynamic energy systems. The model will be a basic building block for design and control of the system, and it allows for numerical simulations of the system, based on the physical features and characteristics of the system
- The second PhD fellow (PhD 2) will focus on developing methods for high performance short-term forecasting of energy supply and demand, since forecasting is crucial for these dynamic systems. The methods will employ AI approaches, and will need to combine information from several different data sources, such as sensors, weather data, consumption data etc.
- Finally, the third PhD student (PhD 3) will focus on the control of dynamic energy systems. The challenges posed by the growing dynamics of energy systems call for adding improved intelligence to control the energy systems. The controller is added on top of the model of the system, and the forecasting is a vital input to this model.
All three PhD candidates will work mostly independently on their specific topic, but they will also be required to work as a team combining energy systems modeling, control and forecasting for specific scenarios.
Work tasks:
The PhD research work will include the following topics and tasks:
Numerical modeling of Dynamic Energy Systems
- Build knowledge about how to numerically model intelligent dynamic energy systems with goals and restrictions on multiple time scales and uncertainty regarding energy input, production and consumption
- Develop a generic and computationally efficient numerical model that can be used to model a wide range of specific cases and
- Dissemination through scientific publications
Machine Learning applied to Forecasting of Energy Production and Consumption in Intelligent Dynamic Energy Systems
- Build knowledge about artificial intelligence-based prognosis of energy supply and demand for intelligent dynamic energy systems on different time horizons and resolutions.
- Develop improved AI-based methods for energy forecasting based on spatially and temporally distributed inputs from innovative sensors.
- Study how different input parameters and their associated uncertainties influence the accuracy of artificial intelligence-based forecasting
- Dissemination through scientific publications
Control of Intelligent Dynamic Energy Systems with Machine Learning
- Build knowledge about artificial intelligence-based control of energy systems with short term uncertainty and multiple long and short-term goals and restrictions
- Develop generic control system strategies based on artificial intelligence for dynamic energy systems with goals and restrictions on multiple time scales
- Quantify effects of the newly developed techniques compared to conventional control strategies based on physics and control theory
- Dissemination through scientific publications
Qualification requirements
The Faculty of Mathematics and Natural Sciences has a strategic ambition to be among Europe’s leading communities for research, education and innovation. Candidates for these fellowships will be selected in accordance with this, and expected to be in the upper segment of their class with respect to academic credentials.
Required qualifications
- Master’s degree or equivalent in a relevant field such as such as Computer Science, Machine Learning, Cybernetics, Autonomous Systems, Energy Systems, Robotics, Statistics, Physics, Mathematics or similar.
- Foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in the Norwegian educational system.
- The PhD candidate must have fluent oral and written communication skills in English.
Desired qualifications
- The PhD candidate would preferably also have one or more of the following:
- Good programming skills in languages, such as Python, C/C++, and/or similar
- Education, training or experience with Machine Learning or Deep Learning algorithms (most relevant for PhD 2 and 3) or with system modeling and system modeling methods (most relevant for PhD 1)
- A good publication track record
- Work experience from the military sector or the energy sector
Grade requirements:
The norm is as follows:
- the average grade point for courses included in the Bachelor’s degree must be C or better in the Norwegian educational system
- the average grade point for courses included in the Master’s degree must be B or better in the Norwegian educational system
- the Master’s thesis must have the grade B or better in the Norwegian educational system
- Fluent oral and written communication skills in English
- English requirements for applicants from outside of EU/ EEA countries and exemptions from the requirements:
http://www.mn.uio.no/english/research/phd/application/application.html
The purpose of the fellowship is research training leading to the successful completion of a PhD degree.
The fellowship requires admission to the PhD programme at the Faculty of Mathematics and Natural Sciences. The application to the PhD programme must be submitted to the department no later than two months after taking up the position. For more information see:
http://www.uio.no/english/research/phd/
http://www.mn.uio.no/english/research/phd/
Personal skills
- Ability to take initiative and come up with new ideas to solve theoretical and practical problems
- Ability to work independently as well as in a team
- Good communication skills
We offer
- Salary NOK 491 200 – 534 400 per year depending on qualifications as PhD Research Fellow (position code 1017)
- Attractive welfare benefits and a generous pension agreement
- Vibrant international academic environment
- Tight connections to FFI and IFE, two nationally leading research institutes within their fields
- Career development programmes, professional courses and workshops
- Oslo’s family-friendly surroundings with their rich opportunities for culture and outdoor activities
How to apply
The application must include:
- Cover letter – statement of motivation and research interests
- CV (summarizing education, positions and academic work – scientific publications)
- Copies of the original Bachelor and Master’s degree diploma, transcripts of records and letters of recommendation
- Documentation of English proficiency
- List of publications and academic work that the applicant wishes to be considered by the evaluation committee
- Names and contact details of 2-3 references (name, relation to candidate, e-mail and telephone number)
The application with attachments must be delivered in our electronic recruiting system, please follow the link “apply for this job”. Foreign applicants are advised to attach an explanation of their University’s grading system. Please note that all documents should be in English (or a Scandinavian language).
Applicants will be called in for an interview.
Formal regulations
Please see the guidelines and regulations for appointments to Research Fellowships at the University of Oslo.
No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo.
According to the Norwegian Freedom of Information Act (Offentleglova) information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure.
The University of Oslo has an agreement for all employees, aiming to secure rights to research results etc.
The University of Oslo aims to achieve a balanced gender composition in the workforce and to recruit people with ethnic minority backgrounds.
Contact information
For further information please contact either:
- Paal E. Engelstad, Professor ITS, Mobile: +47- 41633776, email: paal.engelstad@its.uio.no
- Roy Stenbro, Department Head Wind Energy, IFE, Mobile: +47 92030992, email: roy.stenbro@ife.no
- Narada D. Warakagoda, Principal Research Scientist, FFI, Mobile: +47-48020811 email: Narada-Dilp.Warakagoda@ffi.no
For questions regarding the recruitment system, please contact HR Adviser Torunn Standal Guttormsen, e-mail: t.s.guttormsen@mn.uio.no
About the University of Oslo
The University of Oslo is Norway’s oldest and highest rated institution of research and education with 28 000 students and 7500 employees. Its broad range of academic disciplines and internationally esteemed research communities make UiO an important contributor to society.
The Department of Technology Systems (ITS) is a newly established department at the Faculty of Mathematics and Natural Sciences at the University of Oslo. ITS has taken over the activities at UNIK from January 2017. The Institute is located in the Kjeller Research Park, which is one of the largest research and development centers in Norway. ITS collaborates with the research institutes at Kjeller, and with industry, while it is also tightly integrated with complementary activities at UiO in Oslo. The department has two sections: section for energy systems and section for autonomous systems and sensor technologies. An important goal of ITS is to provide wider opportunities at UiO within applied technologies.