PhD Research Fellowships in Energy Informatics, 2 positions
Two PhD Research fellows are available at Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Norway. The fellowships are for a period of 3 years. Starting date preferably no later than 01.02.2018.
No one can be appointed for more than one PhD Research Fellowship period at the University of Oslo.
The two research fellows will be working in the project SmartNEM (“Smart Community Neighborhood – driven by energy informatics”) funded by the Research Council of Norway. SmartNEM is a joint project with University of Stavanger, Norway. The project will employ a total of seven PhD scholars of which three will be employed by University of Oslo UiO). One of the PhD scholars supported by the SmartNEM project will be supervised in collaboration with the Faculty of Computer Sciences at the Østfold University College in Halden and the Norwegian Centre of Expertise Smart Energy Markets (NCESmart). An industrial advisory board with members including Statnett (the Norwegian TSO), Lyse Energy, DNV-GL and the NCE Smart cluster will offer industrial guidance to the PhD scholars.
The vision of the research is to exploit state-of-the-art ICT methods, tools and techniques for the future sustainable energy systems. We have particular interest in the following technologies for efficient and secure energy systems: fog computing, machine/deep learning, data analytics, blockchain and software defined principles. We will develop new models and algorithms to provide the power grid operators with intelligent energy management with privacy preservation in local regions.
We have defined the following topics for the two PhD fellows.
Topic 1: Secure and privacy-preserved system for smart home and neighborhood. We will identify the new cyber security and privacy challenges in the smart neighborhood. Then, we will propose new solutions to defend against cyber attacks. Blockchain can be a potential technology to ensure secure peer-to-peer energy exchange in the local neighborhood.
Topic 2: Machine learning and deep learning for energy forecasting. Precise forecasting is very important for the stability of the future energy systems. We will study predictive analytics techniques for power stability, including regression, neural networks and other machine learning approaches for real-time and historical analytics. In addition, the renewable energy forecasting will be an important problem in this topic. This PhD project will be done in collaboration with Østfold University College and Smart Innovation Norway in Halden, Norway.
Please indicate in the application which of the positions you would like to be considered for, and in priority order if more than one.
For further information about our openings, see our webpage.