PhD Position in Computer Vision
The Department of Computer Science (IDI) (http://www.ntnu.edu/idi) at the Faculty of Information Technology and Electrical Engineering (http://www.ntnu.edu/ie) of the Norwegian University of Science and Technology (NTNU) has a vacancy for a PhD fellow in Computer Vision, funded by the NTNU Digital programme (https://www.ntnu.edu/digital).
PhD fellowships at NTNU are normally awarded for up to 3 years (with no teaching duties) or up to 4 years with 25% of the time spent on specified work (e.g., teaching duties). This work is primarily teaching assistance at the bachelor’s and master’s degree levels. These duties are commonly distributed over the 4 years. Whether the extra year with specified work is given or not, is decided on a case-by-case basis, depending on the department’s resource needs and an evaluation of what such work the applicant may be capable of performing.
Information about the department
The Department of Computer Science (IDI) has about 200 employees including 31 professors, 34 associate professors, 13 adjunct associate professors, 15 assistant professors, 14 postdoctoral researchers, approximately 90 active PhD students, and 22 technical/administrative staff. More than 7,000 students study one or more courses at the department each year. IDI has offices in both Trondheim and Gjøvik, and the position described in this announcement will be carried out at both locations, particularly affiliated with the Visual Computing Lab in Trondheim (http://www.idi.ntnu.no/grupper/vis/) and the Norwegian Colour and Visual Computing Laboratory (http://www.colourlab.no) in Gjøvik. To find out more about the department's research and educational activities please visithttps://www.ntnu.edu/idi/research/ andhttps://www.ntnu.edu/idi/studies/.
In computer vision, the measurement of object similarity is the key ingredient for operations such as object recognition, detection and retrieval. As objects are commonly represented by proxies called object descriptors, similarity evaluation is based on designing robust and accurate object descriptors and good distance functions between such descriptors. State-of-the-art 3D object descriptors are mainly concerned with object shape. We argue that in order to globally address the above operations, one must consider both shape and spectral information conjointly, and in order to do so new descriptors and distance functions are needed. The challenging fundamental computer science research required for this will subsequently enable applications in a broad range of fields, such as cultural heritage (e.g. reassembly from fragments, erosion measurement), medicine (e.g. pathological object detection), and autonomous vehicles (object detection and tracking for navigation).
We believe that by integrating the shape and spectral components, building on our state-of-the-art previous work in each of them, we can achieve significant progress in the following key objectives:
- Develop novel spectral + shape descriptors and distance functions
- Validate these using appropriate data sets
- Apply them in areas such as estimating the degradation of cultural heritage objects, and reassembling cultural heritage objects from fragments
Further specification of the topic and project plan will be done in collaboration between the selected candidate and the PhD supervisors, Prof. Jon Y. Hardeberg and Prof. Theoharis Theoharis.
Requirement and qualifications
The candidate must have:
- Master’s degree (or equivalent) within the field of computer science, electrical engineering, colour imaging, or a related discipline, with an average grade of B or better.
- Good programming skills.
- Written and oral fluency in English. 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.
The following qualifications are desired:
- Knowledge and experience with computer vision.
- Knowledge and experience with spectral and 3d imaging
- High level of personal responsibility, initiative, and ability to work in a project team and independently.
- Good organisation and communication skills with interest in wider context of own research and ability to engage in cross-disciplinary teams.
- Eagerness to disseminate research results through publications and presentations at international conferences.
All candidates must be able to secure a residency and work permit in Norway. The university is committed to a policy of equal opportunity in employment practices, and we particularly would like to encourage female candidates to apply.
The regulations for PhD programs at NTNU state that a Master degree or equivalent with at least 5 years of studies and an average grade of A or B within a scale of A-E for passing grades (A best) for the two last years of the MSc is required and C or higher of the (three first years) BSc. Candidates from universities outside Norway are kindly requested to send a Diploma Supplement or a similar document, which describes in detail the study and grade system and the rights for further studies associated with the obtained degree: http://ec.europa.eu/education/tools/diploma-supplement_en.htm
Terms of employment
The appointment of the PhD researcher will be made according to Norwegian guidelines for universities and university colleges and to the general regulations regarding university employees. Applicants must agree to participate in an organized doctoral study program within the period of the appointment and have to be qualified for the PhD-study.
The position is in code 1017, salary grade 50 in the Norwegian Government salary scale, gross NOK 436 900 per year, before tax. A deduction of 2% is made as a statutory contribution to the Norwegian Public Service Pension Fund.
The national labour force must reflect the composition of the population to the greatest possible extent. NTNU wants to increase the proportion of women in its scientific posts. Women are encouraged to apply.
A mobility scheme between Trondheim and Gjøvik will be agreed where the candidate spends approximately half time in each campus. International mobility will also be encouraged.
Applications will be processed through the university online application system https://www.ntnu.no/ledige-stillinger. The application must include (in electronic form):
- A cover letter (1 page) explaining your motivation and how your skills and experience relate to the research focus of the position.
- A draft research proposal outlining a possible approach (1-3 pages). Candidates are encouraged to contact the supervisors for more information about the project context.
- CV with list of publications (if any)
- Relevant transcript of grades, diplomas, and certificates
- A description of Master thesis work (1 page)
- At least two letters of reference, preferably from academic supervisor(s), with contact details
- Certificate documenting English language proficiency (any one of the following: IELTS, TOEFL, Cambridge Certificate in Advanced English (CAE), Cambridge Certificate of Proficiency in English (CPE)).
Incomplete applications will be rejected.
Potential successful candidates will be interviewed via Skype or other means.
The application must be sent electronically as one combined PDF file via this page (Jobbnorge.no).
For further information
For information about processing of applications, please contact Senior Executive Officer Anne Kristin Bratseth, Phone (+47) 73 59 67 15, e-mail: email@example.com
Mark the application: IE 066-2018
Application deadline: 04.05.2018
About this job
- Deadline Saturday, May 5, 2018
- Employer NTNU - Norwegian University of Science and Technology
- Municipality Trondheim
- Place of service Trondheim
- Jobbnorge ID 151096
- Internal ID 2018/10876 (IE 066-2018)
- Scope Fulltime
- Duration Temporary
- 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!