The University of Agder has more than 1400 employees and 13 000 students. This makes us one of the largest workplaces in Southern Norway. Our staff research, teach and disseminate knowledge from a variety of academic fields. Co-creation of knowledge is our common vision. We offer a broad range of study programmes in many fields. We are situated at two modern campuses in Kristiansand and Grimstad respectively.
We are an open and inclusive university marked by a culture of cooperation. The aim of the university is to further develop education and research at a high international level.
PhD Research Fellow in Renewable Energy
The University of Agder invites applications for a PhD fellowship in Renewable Energy within the project “Development of Intelligent Short-term Power Forecasting System for Large Wind Power Plants”, for a period of 3 years. The position is linked to The Department of Engineering Sciences. The tentative starting date is March 2019, or as negotiated with the faculty.
The Department of Engineering Sciences has more than 110 employees in scientific positions, and more than 1500 students at all levels. A variety of research is conducted within all the groups; Mechatronics, Renewable Energy, Civil Engineering and Industrial Economy, and we also welcome interdisciplinary projects within the department or with other research groups. This position will be associated with the Renewable Energy group. We have a broad international cooperation, and close collaboration with industrial partners and public services in the region. This is a benefit both for teaching and research. The partners contribute with assignments, competence and resources.
Research work and context
Wind power sector plays a significant role in the global clean energy scenario. Being a stochastic phenomenon, speed and direction of wind changes rapidly within short intervals of time. This causes uncertainty in the generation expected from wind farms, making the management of the grids with high wind energy penetration rather challenging. For addressing this issue, the wind farm operators may have to commit their production in different time periods in advance to the utilities. Hence, a reliable power forecasting system is in great demand within the wind energy industry.
Physical models are commonly used for the short-term wind power forecasts. This approach combines the outputs from the Numerical Weather Prediction (NWP) with the wind farm wake models and wind turbine energy models. Accuracies of these physical methods are not very impressive as the errors from individual models can get accumulated, which will then be reflected in the end-to end accuracy of the combined forecasting output. Another limitation of this approach is its incapability in capturing the ‘deep array effect’ in large wind farms.
In this research, a novel AI based approach would be developed for forecasting the output from large wind farms. The PhD project would incorporate the following tasks:
- Analyzing the performance data from large wind farms for understanding the wake induced power loess and identifying the critical parameters influencing the power deficit.
- Developing wind farm performance models using machine learning methods and optimizing the model structures for maximizing the prediction accuracy.
- Downscaling the NWP based wind forecasts and improving its resolution for the wind farm sites using AI methods.
- Combining the high-resolution wind forecasts with the wind farm performance models for developing the combined wind power forecasting system.
The candidate will be enrolled in the PhD specialization in Renewable Energy at the PhD programme in Engineering and Science. The applicant must qualify for admission to this PhD Programme.
The following admission requirements apply to the PhD Program:
- The average grade for courses included in the bachelor's degree (or equivalent) must be C (or equivalent) or higher
- The average grade for courses included in the master's degree (or equivalent) must be B (or equivalent) or higher
- The Master Thesis (or equivalent) must have a grade B (or equivalent) or higher when the candidate is admitted to the PhD program.
Profiency in oral and written English is required. Knowledge of Norwegian or another Scandinavian language is an advantage.
The successful applicant must have written and spoken English proficiency. Applicants from some countries must document their English proficiency through one of the following tests or certificates:
- TOEFL – Test of English as a Foreign Language with a minimum score of 600 on the Paper-based Test (PBT), or a minimum of 92 on the Internet based Test (iBT)
- IELTS – International English Language Testing System, with a result of at least 6.5, with no section lower than 5.5. (only Academic IELTS test accepted)
- CEFR (Common European Framework of Reference for Languages) certificate of at least Level B2.
Please check this website to see if an English test is required. Please note that the English test requirement applies to applicants from most countries according to the list mentioned above. No other English tests or certificates will be approved, and certifications/statements cannot replace an English test.
Further provisions relating to the position as PhD Research Fellow can be found in the Regulations Concerning Terms and Conditions of Employment for the Posts of Post-Doctoral Research Fellow, Research Fellow, Research Assistant and Resident.
The successful applicant should hold a master’s degree from a Norwegian or an accredited foreign university in renewable energy, mechatronics, electrical engineering, mechanical engineering or a similar/related field and have competence in areas relevant to the proposed research.
The following additional requirements should be met:
- Programming skills (Matlab, Python, C++, R).
- Strong academic skills and previous experience in machine learning and data mining.
- Experience in renewable energy modelling, preferably in wind energy.
The applicant must submit an approved project description within three months of appointment. The PhD dissertation must be written in English.
The position places great demands on the applicant’s capacity for independent and goal-oriented work. Applicants will be assessed on the basis of academic backgrounds and previous research and development experiences in the related fields.
Short-listed applicants will be invited for interviews. With the applicant’s permission, UiA will also conduct a reference check before appointment.
The position is remunerated according to the State Salary Scale, code 1017 Research Fellow, salary NOK 449 400 gross per year. A 2 % compulsory pension contribution to the Norwegian Public Service Pension Fund is deducted from the pay according to current statutory provisions.
The Norwegian Public Service is committed to reflecting the diversity of society, and the personnel policy of the University of Agder aims to achieve a balanced workforce. All qualified persons are therefore encouraged to apply for the position, irrespective of cultural background, gender, age, or disability.
Appointment is made by the University of Agder’s Appointments Committee for Teaching and Research Positions. The successful applicant will have rights and obligations in accordance with the current regulations for the Public Service.
Submit your application and CV online. Please click on the link “Apply for this job”. The following documentation should be submitted as attachments to the online application:
- A letter of application which includes a rationale for applying for the position together with a brief description on the skills and experiences in programming/machine learning/data mining/renewable energy modeling.
- Certificates and grades for all post-secondary education, up to and including bachelor's level, master's degree/higher degree certificate.
- Applicants with foreign higher education must attach an official description of the grading system used at the issuing institution.
- List and links of the applicant's scientific publications, if any.
- A short tentative research proposal of not more than 2 pages, that highlights the background and research design for the intended study.
Please do not submit large documents, like thesis or publications. If any additional information is required, the applicant will be informed latter.
Original documents must be presented for verification to the University of Agder. Successful candidates will be asked, normally during the interview, to ensure that the issuing university submits documents in a sealed envelope directly to UiA or provide access to their documents online, which allows UiA to verify the authenticity of these electronic documents via a secure website hosted at the issuing university (contact person at UiA will be provided later for certain candidates).
The applicant is fully responsible for submitting complete documentation. Without complete documentation we cannot, unfortunately, include you in the assessment process. All documentation of education must be in the original language and in English, Norwegian, Swedish, or Danish (if the original language is not one of these). The application should include a translation, which should preferably be from the issuing university. Additional documentation must be in English, Norwegian, Swedish or Danish.
Closing date: 07.01.19
For further information please contact Professor Sathyajith Mathew, tel. +47 37 23 31 35, e-mail firstname.lastname@example.org or Assistant Head of Department Tom Viggo Nilsen, tel. +47 37 23 32 55, e-mail email@example.com.
In accordance with §25(2) of the Freedom of Information Act, applicants may request that they are not identified in the open list of applicants. The University, however, reserves the right to publish the name of applicants. Applicants will be advised of the University’s intention to exercise this right.
- Søknadsfrist7. januar 2019
- ArbeidsgiverUniversity of Agder
- Intern-IDRef. 245/18
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