NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY -NTNU

PhD Fellowship within Machine Learning

Location
Trondheim, Norway
Posted
23 Dec 2017
End of advertisement period
15 Feb 2018
Ref
IE 140-2017
Contract Type
Temporary
Hours
Full Time

The Faculty of Information Technology and Electrical Engineering (http://www.ntnu.edu/ie) at the Norwegian University of Science and Technology (NTNU) has a vacancy for a 100% position as PhD fellow within Machine Learning for anomaly detection in electric power transmission and high-voltage distribution systems at the Department of Computer Science (IDI) (http://www.ntnu.edu/idi).
 
The appointment is for a term of 3 years. This is a researcher training position aimed at providing future researchers the opportunity of academic development in the form of a doctoral degree.

Information about the department

The Department of Computer Science (IDI) has about 220 employees including 33 full time professors, 60 associate/assistant professors, 19 adjunct professors, 37 postdocs/researchers, 90 PhD students and 22 technical/administrative staff. The department has had research and educational programs in Artificial Intelligence and Machine Learning since the early nineties.
 
The department’s research in machine learning contributes to the state-of-the-art of individual methods and algorithms as well as combinations of methods targeting particular tasks. For example, combining data-intensive methods with knowledge-based methods to produce user explanations for decision support. Thus far, our strongest contributions to the international research front have been within Bayesian learning and probabilistic reasoning, evolutionary learning and neural networks, and instance-based learning and case-based reasoning. In addition, we have ongoing activities at a high international level within large-scale data and information management. Over the last years there has been an increased interest in combined methods, e.g. integrating probabilistic and instance-based learning methods, or combining deep neural networks with reinforcement learning, applied to large volumes of data.
 
Machine Learning for power system time series analysis
The PhD position will be part of the research project EarlyWarn (https://www.sintef.no/en/projects/earlywarn/), an interdisciplinary collaboration between SINTEF Energy Research (Project lead), IDI, NTNU’s Department of Electric Power Engineering, and several external partners. The goal of the PhD position is to work on AI methods that can improve the real-time understanding of the power system, focusing on proactive detection of system instabilities and incipient failures.

The primary aim of the PhD project is to develop methods for a machine learning system that detects anomalies in the power system, using data from sensors of very high time-resolution. The most relevant sensors are phasor measurement units (PMUs, which typically sample in real-time at 50 Hz), and power quality analyzers (which may deliver up to thousands of observations per second). The project partners have considerable experience in manually analyzing such data, and this experience and (implicit) knowledge should as far as possible be integrated into the machine learning methods. To this end the project will utilize scalable machine learning and big data analytics methods to provide insight into the data, and should allow the user in real time to assess the state of the power system. The applicability of the machine learning system will be explored using real-life data that is already being collected, and an important success-criteria is to show value in the machine learning system in terms of detected anomalies that exceed the cost of collecting and storing the data streams.

Job description

The PhD fellow will be a member of the Data and Artificial Intelligence group and will participate in relevant research activities within the scope of the project. For this reason, a successful candidate is expected to perform excellent research within AI, machine learning, data analytics or related areas.

Qualifications

A master's degree in Computer and Information Science or equivalent with very good results is required, with an average grade B or better as measured in ECTS (European Credit Transfer System) grades, or an education at the equivalent level. A solid knowledge of machine learning or related methods is essential, and a research-oriented master thesis within one of these or related areas is expected. Good programming skills are required.

Due to collaboration with external partners (domain experts), it is beneficial, but not required, that the chosen candidate masters a Scandinavian language. Applicants who do not do so must provide evidence of good English language skills, both written and spoken. The following tests can be used as such documentation: TOEFL, IELTS or Cambridge Certificate in Advanced English (CAE) or Cambridge Certificate of Proficiency in English (CPE). Minimum scores are:

  • TOEFL: 600 (paper-based test), 92 (Internet-based test)
  • IELTS: 6.5, with no section lower than 5.5 (only Academic IELTS test accepted)
  • CAE/CPE: grade B or A

In extraordinary circumstances, formal documentation of language skills can be relinquished. In such cases the candidate’s language skills will be assessed in a personal interview.

Formal regulations

Appointments are made in accordance with the regulations in force regarding terms of employment for PhD candidates issued by the Ministry of Education and Research, with relevant parts of the additional guidelines for appointment as a PhD candidate at NTNU. Applicants must undertake to participate in an organized PhD programme of study during their period of employment. The person who is appointed must comply with the conditions that apply at any time to employees in the public sector. In addition, a contract will be signed regarding the period of employment, including duty work if relevant.

Applicants must be qualified for admission as PhD students at NTNU. See http://www.ntnu.edu/ie/research for information about PhD studies at IE, NTNU. Together with the application, include a description of the research work that is planned for completion during the period of the grant.

Salary conditions

The position is in code 1017 Stipendiat, minimum salary grade 50 in the Norwegian State salary scale, typically in salary grade 50-57, gross NOK 436.900,- to NOK 490.900,- per year, depending on qualifications. A deduction of 2 % is made as a statutory contribution to the Norwegian Public Service Pension Fund.

General information

We can offer an informal and friendly workplace with dedicated colleagues, academic challenges in an international environment, attractive schemes for housing loan and insurance and pensions in the Norwegian Public Service Pension Fund

The Faculty of Information Technology and Electrical Engineering want to attract outstanding and creative candidates who can contribute to our ongoing research activities. We believe that diversity is important to achieve a good, inclusive working environment. We encourage all qualified candidates to apply, regardless of the gender, disability or cultural background.

NTNU wants to increase the proportion of women in its scientific posts. Women are encouraged to apply. The appointment is subject to the conditions in effect at any time for employees in the public sector.

As far as possible, the State workforce should reflect the diversity of the population. Goals of our personnel policy therefore include a balanced distribution in terms of age and gender as well as recruitment of people of immigrant background.
Under Section 25 of the Freedom of Information Act, information about the applicant may be made public even if the applicant has requested not to have his or her name entered on the list of applicants.

The application requirements:

The application must contain:

  •  Curriculum vitae (CV) with information about the candidate’s prior training, exams, and work experience 
  • Certified copies of transcripts and diplomas 
  • Applicants from universities outside Norway are kindly requested to send a diploma supplement or a similar document, hich describes in detail the study and grading system and the rights for further studies associated with the obtained  degree
  • A Statement of Purpose (typically between 2 and 4 pages in total) including
    • A short presentation of the motivation for a PhD study Suggestions for how AI or machine learning methods can be utilized for anomaly detection in the power  system,preferably including ideas for which methods to further explore in the study period. 
    • How the competence of the applicant can contribute to solving these challenges
  •  Names and contact information of at least 2 reference persons 
  • A copy of the master thesis (in PDF), or, for those who are near to completion of their MSc, an extended abstract   combined with a  statement of how and when the applicant plans to complete the thesis (1 page)

Incomplete applications will be rejected.

The application must be sent electronically as one combined PDF file via this page http://www.jobbnorge.no/). Potential successful candidates will be interviewed via Skype or other means.

For further information about the fellowship, please contact:Professor Helge Langseth, e-mail: helge.langseth@ntnu.no,
Associate Professor Heri Ramampiaro, e-mail: heri.ramampiaro@ntnu.no, Research Manager Boye Annfelt Høverstad, e-mail: boye.a.hoverstad@sintef.no or Head of the department Professor John Krogstie, e-mail: john.krogstie@ntnu.no.

For information about processing of applications, please contact Senior Executive Officer Anne Kristin Bratseth, phone +47 73 59 67 15, e-mail: anne.kristin.bratseth@ntnu.no.

Mark the application IE 140-2017

Deadline for application:15.02.2018

About this job

  • Deadline Thursday, February 15, 2018
  • Employer NTNU - Norwegian University of Science and Technology
  • Website
  • Municipality Trondheim
  • Place of service Trondheim
  • Jobbnorge ID 146135
  • Internal ID IE 140-2017
  • Scope Fulltime
  • Duration Temporary

About applications

  • Applications on this job are registered in an electronic form on jobbnorge.no
  • You must complete: Standard CV
  • Please refer to where you first saw this job advertised!

Similar jobs