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Professor/Associate Professor in Statistical Machine Learning for Signal Processing (IE 137-2018)

Signal processing concerns the analysis, synthesis and modification of information carrying signals. The signals are often data representations of physical events, usually based on sensor and measurement data, such as audio, video, seismic measurements, medical signals like e.g. ultrasound and ECG, and biological data. The connection between the data and what they represent is thus central within the discipline.

Machine learning is tightly connected to statistical learning, and concerns methods for designing systems for e.g. classification and regression, where the systems learn from data. Signal processing and statistical machine learning have for a long time been partially overlapping disciplines, and machine learning methods constitute an increasingly important area of signal processing. In order to strengthen the profile of the Signal Processing group towards statistical machine learning and to enable opportunities for opening new application areas, a position targeting machine learning for analysis, classification, prediction and data mining of (large amounts of) sensor data, typically measurements in time and/or space will be established.

The successful candidate is expected, together with existing faculty, to jointly develop a world-class research community within statistical machine learning.

The position announced here will be affiliated with the Department of Electronic Systems at NTNU’s Faculty of Information Technology and Electrical Engineering.

About the department

The Department of Electronic Systems (Institutt for elektroniske systemer/IES) has currently 45 professors/associate professors, 70 PhD students, 25 researchers/Post.Doc fellows and 25 engineering/administrative employees.

IES has the principal responsibility for education and research in electronics at NTNU. IES research portfolio comprises the areas of statistical signal processing, wireless communication, marine acoustics/sub-sea communications, multimedia and speech technology, micro- and nanotechnology, sensors, and medical technology.

IES has an international profile with a strong ambition to be a leading international research unit within its field, and the department is involved in numerous externally funded research projects. IES works strategically to increase our research activities, and we therefore aim to strengthen our capability and capacity in this strategically important field.

The Signal Processing group at IES currently comprises 6 professors/associate professors and 2 adjunct professors/associate professors, undertaking education and research with applications in digital communications and sensor networks, multimedia and speech technology and medical technology. A related position within statistical machine learning for speech technology is currently being announced by the department. That position has particular emphasis on statistical pattern recognition with primary applications in speech and language.

Job description

The professor/associate professor is expected to play a leading role in research and research-based education in statistical machine learning in cooperation with existing staff at IES. In particular, close collaboration with the Signal Processing group's professors active in machine learning for communications and speech technology is expected. The successful candidate is also expected to establish collaboration with relevant colleagues at other departments at NTNU and within NTNU’s strategic research areas, including other groups at NTNU active in machine learning, in particular the Telenor-NTNU AI Lab.

The focus area of this position is towards statistical machine learning applied to analysis, classification, prediction and data mining of (large amounts of) sensor data, typically measurements in time and/or space. Examples comprise sound, vibration measurements, seismic observations, natural images, medical data like EEG and ECG, temperature data, CO2 measurements and position data. Application areas include error and fault prediction, extraction of "hidden" information, image classification, medical analysis, decision support systems and the interplay between machine learning and existing mathematical models of physical systems and phenomena. The successful candidate will need to have experience and knowledge at a high international level within statistical machine learning and its utilization in relevant application areas.

Research activities are expected to have a strong international profile and impact, with a long-term perspective and to be concentrated around fundamental research challenges and development of new enabling technologies.

The research activities at the department rely mainly on external funding, and the development of educational programs may also receive external funding. The successful applicant is expected to engage extensively in applications for external research funding, e.g. from the Research Council of Norway, European research and educational agencies, the industry sector, and other available sources.

MSc and PhD candidates from the Electronic Systems study programs are expected to be competitive in an international job market. The professor/associate professor will contribute toward the department’s educational profile and promote an excellent learning environment, in collaboration with colleagues, students and external stakeholders. Specifically, the professor/associate professor is expected to teach one course of the department’s MSc program and a specialization course at MSc or PhD level. She or he should supervise MSc students, PhD candidates and postdoctoral fellows.

In addition to research and education, the professor/associate professor is expected to disseminate relevant parts of the research to a wider audience.

The professor/associate professor is also expected to participate in the formal management of research, education and other relevant areas of activity in agreement with the head of department.


The applicant is required to have a doctoral degree or equivalent in a relevant area and demonstrate professional qualifications in essential areas of statistical machine learning as defined in the job description, with profound scientific competence within one or more of these areas. Documented expertise as well as potential for excellence will be considered in the assessment of candidates. The candidate's personal aptitude and motivation for the position will be assigned great weight in the selection process.

Qualifications for the professorship

To be employed as full professor the candidate must be an internationally renowned researcher and must have demonstrated capability to initiate and lead research on a high international level. The candidate must have international experience and have a strong publication list in international journals and other relevant publication channels. Documented capability to attract external research funding will be emphasized.

Qualifications for the associate professorship

For a position as associate professor, the applicant should have a good publication record in terms of papers in peer-reviewed journals and other relevant international publication channels. Successful acquisition of external research grants, experience with research leadership and relevant collaboration with industry should be documented and will be rated positively. The candidate should have a research potential which makes it likely to qualify for a full professorship within five years of employment, even with normal teaching duties.

The applicant should demonstrate the ability to develop educational activities and the learning environment. She or he should have experience with supervision of master and doctoral students or similar experience qualifying for such work.

The applicant should demonstrate communicative skills that qualify for excellent teaching, supervision and dissemination, and have good collaboration skills necessary for joint interdisciplinary projects. The Signal Processing Group has a long-standing and good tradition for collegial collaboration, and the ability for teamwork will be central.

 Industrial experience and relevant collaboration with industry will be considered as highly beneficial.

Concerning general criteria for the position, we refer to Regulations concerning appointment and promotion to teaching and research posts: https://www.regjeringen.no/no/dokumenter/regulations-concerning-appointment-and-promotion-to-teaching-and-research-posts/id2519258/

Application requirements

The application should contain:

  • CV including information relevant for the qualifications and a full list of publications with bibliographical references

  • Diplomas and references

  • The most important publications that are relevant for the evaluation of the applicant’s qualifications (maximum 10 publications)

  • A brief description of the scientific/technological relevance of the candidate's research

  • Research proposal for the first five years of employment (maximum 10 pages)

  • Information about educational experience, including development of study programs, curricula, teaching experience, and development of teaching methods and the learning environment. See “Documentation of an applicant’s pedagogical qualifications”: http://www.ntnu.edu/vacancies/pedagogical-qualifications
  • Information about dissemination activities

  • Other documents which the applicant would find relevant

Joint works will also be evaluated. If it is difficult to identify the contributions from individuals in a scientific collaboration, applicants are to enclose a short summary of her/his contribution.

Following the application deadline, a shortlist of applicants will be drawn up, and all applicants will be informed whether they are placed on the shortlist. Shortlisted applicants will be evaluated by an international expert committee. The top candidates from this evaluation will be invited for interviews and trial lectures. The evaluation will take into account not only the accumulated academic production but also the applicant’s potential for scientific development and personal qualities. Young researchers are therefore encouraged to apply. NTNU wishes to increase the number of women on its workforce, and women are specifically encouraged to apply. Strategic considerations may be taken in the final selection of candidates.

Formal regulations

If the candidate does not have prior formal pedagogical qualifications in university-level teaching, the candidate must complete a recognized course which gives a pedagogical qualification within the first two years of employment. NTNU offers such courses.

Proficiency in the English language must be documented. Proficiency in a Scandinavian language will be rated positively. New members of the academic staff who do not master a Scandinavian language are expected to achieve proficiency in Norwegian within three years of employment. This proficiency should correspond to level three in the “Norwegian for Foreigners” courses provided at NTNU.

Diversity is important to achieve a good, inclusive working environment. We encourage all qualified applicants to apply, regardless of gender, disability or cultural background.

The appointment is to be made in accordance with the regulations for State Employees and Civil Servants in Norway. The candidate must adhere to regulations that concern changes and developments within the discipline and/or the organizational changes concerning activities at NTNU.

Further details about the position can be obtained from Head of Department Odd Kr. Ø. Pettersen, e-mail: odd.k.pettersen@ntnu.no

The position as professor is normally remunerated with gross salary from NOK 605 400 to NOK 1 273 700 a year. The position as associate professor is normally remunerated with gross salary from NOK 485 700 to NOK 722 000 a year. 2% of the salary will be deducted as an obligatory premium to the Norwegian Public Service Pension Fund.

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.

Applications are to be submitted electronically through www.jobbnorge.no. Preferably, all attachments should be combined into a single file.

Reference no: IE 137-2018.

Application deadline: 31.8.2018.

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  • Søknadsfrist
    31. august 2018
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    NTNU - Norwegian University of Science and Technology
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