OsloMet – Oslo Metropolitan University is Norway’s third largest university, with nearly 22,000 students and 2,200 employees. Through the students we educate and the research we produce, the university has a direct impact on society, both in Norway and beyond. The university has two campuses, one in central Oslo and another a short distance from the city in the municipality of Lillestrøm. OsloMet is home to some of Norway’s largest and oldest educational programmes, including degree programmes in nursing, engineering and teacher education. The university is also a hub of research and technological innovation aimed at strengthening and sustaining the Norwegian welfare state.
The Faculty of Technology, Art and Design (TKD) offers higher education and research and development (R&D) activities within technical subjects, arts and design. The Faculty has approximately 4.000 students and 400 staff members and is situated at Pilestredet Campus in downtown Oslo and at Kjeller Campus in Viken.
The Department of Computer Science offers three bachelor’s degree programmes, a master’s degree programme, and is part of a cross-departmental PhD program. Academic staff at the department are pursuing research in a wide range of areas including computer science, the natural sciences, and innovation and management. Both students and researchers are also involved in an increasing number of interdisciplinary initiatives across the university.
PhD Fellowship position in Deep Generative Models of Eye-Tracking data
The Department of Computer Science has a vacant PhD Fellowship position in Deep Generative Models of Eye-Tracking data. PhD fellowship is connected to the Artificial Intelligence Lab (AI Lab) research group and Nordic Centre for Sustainable and Trustworthy Artificial Intelligence Research (NordSTAR).
Area of research
The position will focus on some of the main tasks of the Virtual-Eye project, namely in generative adversarial networks and stochastic modeling of eye-gaze trajectories.
About the project: As a door to the outside world and by virtue of millions of years of biological evolution, the eyes exhibit a complex scanpath behavior that is highly efficient at finding objects of interest. Novel search algorithms inspired by eye-gaze trajectories admit a list of potential applications in autonomous robotic search where the search time is critical, such as target detection in surveillance applications and visual scan for self-driving cars. Furthermore, the study of gaze has many important applications to medicine and psychology. It is observed that there is a gaze signature unique to each person which also provides information about personality traits, IQ and skills in a particular field, as well as diagnosing attention deficit hyperactivity disorder, Alzheimer’s and autism. Mathematical research tends to forego that the eyes are intrinsically linked with the cognitive process. Here, eye movements are often treated as a simple way to forage for visual information. That is, however, a crude simplification: human scan-paths reflect more than just a strategy to forage for visual information and they seem to be better than other searching strategies such as Lévy flights which operate with simplistic assumptions. Both these mathematical processes are seemingly universal in explaining the foraging behavior in processes as diverse as the hunting movement of albatrosses and sharks, the movement of swimming bacteria or the exploration of the Walt Disney Resort by children. Yet, paradoxically, the debate is still ongoing what its general form actually is. Our hypothesis in the "Virtual-Eye" project is that some of the added complexity of gaze trajectories, when compared to Lévy flights or intermittent processes, are designed to optimize the search for visual information.
PhD fellow will carry out research and experiments with human participants within the frames of the project in close collaboration with the supervisors and other staff members at the department. The research will intersect AI methods, with focus on generative adversarial networks and other machine learning methods, statistical learning and theoretical aspects of statistics and probability theory, and eye-tracking. In addition to the research tasks, the fellow will disseminate the projects results and outcomes through appropriate channels, help in administrative requirements as project partner, and help in the supervision of master projects related to the Virtual-Eye project.
The position is advertised as a 3-year position with 100% research, or a 4-year position with 75% research and 25% other tasks (teaching, supervision and/or administrative work). The goal must be to complete the PhD program/degree within the decided time frame. The decision on a 3- or 4-year position will be discussed as part of the interviews in the hiring process. The start date of the position is 01.10.2023 at the latest.
Qualification requirements and conditions
- Master's degree in Artificial Intelligence, Physics, Computer Science or other related fields. The degree must contain 120 credits (ECTS). All exams for the master's degree must be completed by 30 June 2023.
- An academic profile that suits the department’s needs.
- Good communication skills in English, oral and written.
The following grade requirements are a condition for employment in the position:
- Minimum average grade B on subjects included in the master's degree.
- Minimum grade B on the master's thesis.
- Minimum average grade C on the subjects included in the bachelor's degree. If you have an integrated master's degree, the grades from the first three standard years of the degree will be assessed.
Admission to the doctoral program in Engineering Science at the Faculty of Technology, Art and Design within three months of employment is a prerequisite for the position. If you already have a doctorate in a related field, you will not qualify for the position.
In assessing the applicants, emphasis will be placed on the department's overall needs and the applicant's potential for research within the field.
General criteria for employment in academic positions are covered by the Regulations on employment conditions for positions such as postdoctoral fellow, scientific assistant and specialist candidate.
Preferred selection criteria
- Documented education, work experience and/or research in deep learning methods, machine learning techniques and AI algorithms
- Expertise in generative adversarial networks, and knowledge in recent advances in deep generative models, such as diffusion models Knowledge in advanced statistical modeling methods
- Good abilities in problem solving and interest in disseminating science
- Have an interest and ability to work independently and in a team and in an interdisciplinary academic environment
- Motivation for contribution in the field of research and ability to work goal-oriented
It is important to OsloMet to reflect the population of our region, and all qualified candidates are welcome to apply. We make active endeavours to further develop OsloMet as an inclusive workplace and to make adaptations to the workplace where required. You are also welcome to apply for a position with us if you have had periods where you have not been in employment, education or training.
We can offer you
- An exciting job opportunity at Norway’s third largest and most urban university
- Opportunities for professional development in the AI Lab and within one of OsloMet’s Centers of Research Excellence, NordSTAR
- Beneficial pension arrangements with the Norwegian Public Service Pension Fund
- Beneficial welfare schemes and a wide range of sports and cultural offers
- Free Norwegian language classes to employees and their partners/spouses
- Workplace in downtown Oslo with multiple cultural offers
Practical information about relocation to Oslo and living in Norway.
The position is paid according to the pay scale for Norwegian state employees, position code 1017 PhD fellow, NOK 501 200 per year.
Application
To be considered for the position, you must upload the following documents by the application deadline:
- Application letter describing your motivation and how your professional profile is relevant for this position.
- Copies of diplomas and transcripts for bachelor's and master's degrees (which list subjects and grades) and certificates. Please note that a description of the grading system at the university/country where you took your degree must be attached. This must be an official document with a stamp from your university. Foreign diplomas must be translated into English by the university that issued the diploma.
- If you have not completed your master's degree at the time of application, you must attach a preliminary transcript in English or a Scandinavian language from your university by the application deadline. It is desirable that you also attach an overview of subjects/exams you will complete during spring 2023. Relevant applicants must also send an official confirmation from the educational institution by 30 June 2023, that all examinations for the master's degree, including the master's thesis, have been completed.
- Name and contact information of two references (name, relationship, e-mail and telephone number).
- Scientific work that you want to be assessed.
- Applicants from countries where English is not a first language must submit the result of an official language test. The following applicants are exempt from this language requirement:
- Applicants from EU/EEA countries.
- Applicants who have completed at least one year of study in Australia, Canada, Ireland, New Zealand, Great Britain or the United States. Applicants who hold an “International Baccalaureate (IB)” diploma.
The following language tests are approved documentation: TOEFL, IELTS, Cambridge Certificate in Advanced English (CAE) or Cambridge Certificate of Proficiency in English (CPE). In these tests, you must have achieved at least the following scores:
- TOEFL: 600 (paper-based test), 92 (Internet-based test)
- IELTS: 6.5, where none of the sections should have a lower score than 5.5 (only the Academic IELTS test is accepted).
Official diploma and transcript must be submitted before taking up the position, no later than 01.10.2023. If your educational institution is not able to deliver an official diploma by the deadline, you must submit documentation from the institution that confirms that your master´s degree is completed by the same deadline.
We only process applications sent via our electronic recruitment system and all documents must be uploaded for your application to be processed. The documents must be in English or a Scandinavian language. Translations must be authorized. You must present originals at any interview. OsloMet checks documents, so that you as a candidate will get a real evaluation and fair competition.
The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criteria in the latter law will be prohibited from recruitment to OsloMet.
Other information
If you would like more information about the position, feel free to contact:
- Head of Group Gustavo Borges Moreno e Mello, gustavo.mello@oslomet.no
- Professor Pedro Lind (main supervisor), pedro.lind@oslomet.no
- Professor Anis Yazidi (supervisor), anis.yazidi@oslomet.no
- Professor Sergiy Denysov (supervisor), sergiy.denysov@oslomet.no
If you have administrative questions about the position, please contact hrtkd@oslomet.no.
If you would like to apply for the position, you must do so electronically through our recruitment system.
Deadline for applications: 01.06.2023
Ref.: 23/02251
OsloMet is a Charter & Code certified institution by the EU Commisson holding the right to use the logo HR Excellence in Research (HRS4R). OsloMet is a member of the EURAXESS network supporting a positive work environment for researchers in motion. OsloMet has signed The Declaration on Research Assessment (DORA). DORA recognizes the need to improve the ways in which the outputs of scholarly research are evaluated.
