Stilling:

Professor / Associate Professor in Machine Learning for Renewable Energy

Deadline: October 7, 2018

UiT The Arctic University of Norway

UiT, The Arctic University of Norway has in 2016 established the Arctic Centre for Sustainable Energy (ARC). This is an interdisciplinary centre focusing on Arctic challenges and conditions within renewable energy and greenhouse gas management. The centre will combine expertise in physics, humanities, chemistry, social sciences, applied mathematics, marine biology, computer science, and electrical engineering. The initiative will permeate the university in its entirety and will strengthen existing research activities at UiT within the scope of the centre.


The announced position will strengthen ARC and the Department of Mathematics and Statistics by recruiting an excellent researcher within the field of machine learning for renewable energy. The successful candidate will perform research in close collaboration with other members of ARC.

The Department of Mathematics and Statistics at UiT – The Arctic University of Norway (UiT) has a position vacant for a highly qualified full professor or associate professor in the area of machine learning for renewable energy. A start-up package will be offered, with one doctoral and one postdoctoral position at the professor’s disposal.

Further information about the position is available by contacting:

  • Professor Martin Rypdal, Head of the Department of Mathematics and Statistics
  • Professor Fred Godtliebsen, UiT Machine Learning Group and DMS
  • Professor Arne Smalås, Dean at the Faculty of Science and Technology

The position’s affiliation

The Faculty of Science and Technology consists of the following six departments:

  • Department of Chemistry
  • Department of Computer Science
  • Department of Engineering and Safety
  • Department of Geoscience
  • Department of Mathematics and Statistics
  • Department of Physics and Technology

The Department of Mathematics and Statistics consists of four research groups:

  • Geometry and mathematical physics
  • Algebra
  • Complex Systems Modelling
  • Machine Learning

The department provides education at the Bachelor, Master, and PhD levels. It comprises 17 faculty members and 1 administrative person. The department conducts research and education at a high international level. The members of our academic staff are engaged in several international collaborative projects.

UiT is currently conducting a strategic effort within machine learning, and the research group in machine learning has grown considerably the last few years. The current research fields of the group members are development of the next generation machine learning algorithms based on deep neural networks, kernel machines, graph theoretic approaches, reinforcement learning, multimodal image analysis and domain adaptation. Group-wide applications are within health (analysis of data from electronic health records for decision support systems), remote sensing (change detection and vegetation mapping), and industry (close collaboration with several companies). A collaboration with the new PET-center in Tromsø has recently been established, and the group is involved in diabetes research.

The position’s field of research

The candidate will strengthen the Arctic Centre for Sustainable Energy (ARC) through research on machine learning for renewable energy. Building interdisciplinary excellence will be mandatory in the position. The person holding this position should have experience from interdisciplinary work and have a strong motivation to contribute in the area of renewable energy.

The successful applicant must participate in the teaching at the Department of Mathematics and Statistics, including supervision of Master and PhD projects, in particular within the specializations of Machine Learning and Statistics. Commitment to the department’s standard for teaching quality, recruitment and public outreach activities is expected. As is the commitment to obtaining external research funding.

All candidates must submit a statement of their vision for how they will strengthen ARC and the machine learning activity at UiT. It is underlined that commitment within renewable research is crucial for the present position.

Qualification requirements

We seek a candidate with a background in machine learning or closely related fields (reinforcement learning, intelligent control theory, deep learning, pattern recognition, and computational statistics). The candidate should have the analytical skills needed to exploit data in the decision-making process for design and operation of renewable energy systems. Since reinforcement learning is an important research area at the department, and this methodology has huge potential in design of successful renewable energy systems, candidates within this area are strongly encouraged to apply.

We will consider both experienced researchers as well as highly promising candidates in earlier stages of their careers. The applicant should have teaching skills and experience with supervision of PhD/MSc students will be beneficial, as well as the ability to build networks, create trust, and communicate well. Previous success with research funding is highly valued. The candidate should value to work in an interdisciplinary environment.

You must be fluent in English, and have a good command of Norwegian, Swedish or Danish, or must be willing to learn Norwegian within a reasonable period.

When ranking the candidates, the most crucial ranking criterion is the candidates’ potential to contribute in ARC through machine learning methodology for renewable energy applications. We will also consider how their competences complement those of the current personnel in the Department of Mathematics and Statistics, and in the Machine Learning Group.

Teaching qualifications

Teaching qualification must be documented by submitting a teaching portfolio, see the website for basic pedagogical competence.

In the case of documented teaching experience but lacking teaching portfolio, the applicant should develop a portfolio within a three-year period. Interim appointment based on lacking documented teaching qualifications can be considered, as described below.

For further information about the teaching portfolio and requirements for teaching qualifications, see the website for basic pedagogical competence. By exception, the committee may determine that the applicant’s practical teaching skills is of equal value to formal teaching qualifications.

Interim appointment

In there are no fully qualified applicants for the position, we may make an interim appointment to qualify during a period of three years. If the interim appointment is based on lacking teaching qualifications, the applicant must document such qualifications through developing a teaching portfolio within three years. Based on a new application before the three-year period elapses, a permanent appointment shall be made if the new assessment finds the candidate suitably qualified.

Working conditions

The successful applicant must be willing to participate actively in the ongoing development of the discipline, the department, and the university as a whole. The candidate must participate in teaching at all levels and formats offered by the department. This includes supervision of undergraduate and graduate projects.

The professor/associate professor must contribute to enhance the department’s research within the described areas. There are excellent opportunities to develop research projects in collaboration with other faculty members in ARC and generally at the Faculty of Science and Technology. The professor/associate professor is expected to be successful in raising external research funds.

In general, the professor/associate professor shall spend an equal amount of time on teaching and research and development work, after time spent on other duties has been deducted. As a norm, the time resources spent on administrative duties constitutes 5 % for academic staff in this category of position. The allocation of working hours shall be flexible and allocated on a case-by-case basis.

Details are found in the Guidelines for distribution of working hours for employees in teaching and research positions.

We offer

  • A good working environment
  • Broad opportunity to participate in shaping research and education at the department
  • A start-up package with one doctoral and one postdoctoral position at the professor’s disposal
  • Employees in permanent positions as professor/associate professor have the right to apply for a paid sabbatical (research and development) every fifth year; an arrangement considered to be among the best in the country. Requirements are set in UiT’s Guidelines for the allocation of R&D sabbatical.
  • Good arrangements for pension, insurance and loans in the Norwegian Public Service Pension Fund

The remuneration for Professor is in accordance with the State salary scale code 1013 and Associate Professor in accordance with code 1011. A compulsory contribution of 2% to the Norwegian Public Service Pension Fund will be deducted. In addition to the salary; UiT pays approximately 12.35 % directly to the Pension Fund.

More information about moving to Norway:

http://uit.no/mobility

Assessment

An expert committee will assess the applicants. The committee’s mandate is to undertake an assessment of the applicants’ qualifications based on the material presented by the applicants, and the detailed description drawn up for the position. The applicants who are assessed as the best qualified will be invited for interview. The interview shall among other things, aim to clarify the applicant’s personal suitability for the position. A trial lecture may also be held.

Application

The application must be submitted electronically via the application form available on www.jobbnorge.no and shall include:

  • letter of application
  • CV (containing a complete overview of education, supervised professional training and professional work)
  • diplomas and references
  • form for documentation of teaching qualifications
  • list of works and description of these (see below). The list of works shall contain the following information:
    • author(s), the work’s title
    • for articles: the journal’s name and volume, the first and last page of the article, year of publication
    • for publications: publisher, printer, year of publication, number of pages
  • academic works: The applicant may submit up to ten works that are central to his/her production. The applicant’s doctoral thesis is regarded in this context as one work.
  • vision statement (as described above)
  • list of external funding
  • In addition, the applicant shall provide a description of his/her production stating which works he/she considers the most important and which shall therefore be the main emphasis of the assessment. A brief description of the other listed works shall also be included to demonstrate depth of production. These descriptions shall be an attachment to the application.

All documentation that is to be evaluated must be in English or a Scandinavian language.

General

We make the appointment in accordance with the regulations in force concerning State Employees and Civil Servants, and guidelines at UiT. At our website, you will find more information for applicants.

UiT wishes to increase the proportion of females in senior research positions. In the event that two or more applicants are found to be approximately equally qualified, female applicants will be given priority.

UiT has human resource policy objectives that emphasize diversity, and therefore encourages qualified applicants to apply regardless of their age, gender, functional ability and national or ethnic background. The university is an IW (Inclusive Workplace) enterprise, and emphasize making the necessary adaptations to the working conditions for employees with reduced functional ability.

Personal data given in an application or CV is processed in accordance with the Personal Data Act. You may request not to be registered on the public list of applicants, but the University may decide that your name will be made public. You will receive advance notification in the event of such publication.

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