PhD Research Fellow in Intelligent Recommender Systems Fakultet for teknologi, kunst og design (TKD), Institutt for informasjonsteknologi

PhD Research Fellow in Intelligent Recommender Systems
Fakultet for teknologi, kunst og design (TKD), Institutt for informasjonsteknologi

Oslo and Akershus University College of Applied Sciences (HiOA) is Norway’s largest state University College, with approximately 17000 students and 2000 employees. We conduct research in areas that are important for welfare and value creation, for instance health, education, social sciences, technology and design. HiOA has an academic stimulating environment with a variety of welfare schemes.
Our goal is to become a leading university in the Nordics within the field of applied sciences.

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 2800 students and 240 staff members and is situated at Pilestredet Campus in downtown Oslo and at Kjeller Campus in Akershus.

The Department of Computer Science offers bachelor’s and master’s degree programmes in computer science, qualifying candidates for further studies within engineering, international semester and other further education programmes. The department also offers research and development activities. The department has approximately 40 staff members and 475 students.
PhD Research Fellow in Intelligent Recommender Systems: Enhancing Information Recommender Systems with Machine Learning and Artificial Intelligence methods.

The Faculty of Technology, Art and Design currently has an opening for a PhD position at the Department of Computer Science within Computer, Library and Information Science.

The PhD project should be relevant to the field of Library and Information Science, but with a Computer Science focus. The research problem can be addressed by methods that have been a long-term research focus at the Computer Science department. These methods include artificial intelligence, machine learning (for classification, sentiment analysis, etc.).

The fellowship will be for a period of four years with 25% compulsory work (teaching, lab, or project supervision responsibilities at the department). The PhD position is affiliated with the PhD program in Library and Information Science, which currently has around 20 research fellows.

The fellowship requires admission to in the PhD-program "Library- and Information Science" at Faculty of Social Sciences, Institute for Archivistics, Library and Information Science, which will be the awarding institution.

Area of research

The area of research is recommender systems and information retrieval systems. The project will investigate the enhancement of Intelligent Recommender Systems with novel artificial intelligent tools such as Deep Learning. The data available for this project consists of book-records with both professional meta-data and user-generated content.


The application letter shall include the applicant’s motivation for applying to this fellowship as well as a short overview of the qualifications, technical skills, experience, and personal skills that make you an attractive candidate for a successful and productive completion of this work.


Master degree in Electrical Engineering or Computer Science. Other degrees can be considered depending on qualifications.
Some Experience/ knowledge within machine learning artificial intelligence / recommender systems/information retrieval systems.
Experience with Deep Learning will be beneficial for this work.
Recognized publications will be an advantage
The candidate must be fluent in English, both written and spoken.

Desired skills

Strong analytical skills
Excellent communication skills and ability to collaborate in interdisciplinary teams under supervision
Ability to work independently, take initiative, plan, dimension and direct own work
Some practical experience with wireless networks and/or hardware electronics will be an advantage
We offer

Opportunity to contribute to research and development of as well as collaboration across different research groups at the Computer Science Department and the Department of Archivistics, Library and Information Science
Opportunity to collaborate with International Research Network Opportunity to collaborate with productive researchers.
A dynamic working environment with challenging tasks in an area of substantial development
Benefits such as flexible hours and various welfare schemes
Read more about working at HiOA
Expert committee and required documentation

All application documents submitted should be in English or a Scandinavian Language.

As an applicant you will be assessed by an expert committee. In addition to the online application, you must upload the following documents:

application letter, CV and copies of diplomas/certificates including all grades
copy of MSc thesis and any other papers or publications you wish to be considered, including a full list of any publications you may have.
Scientific publications are not required of a PhD candidate, however, any publications will be an advantage
names and contact details of 2-3 references (name, relation to candidate, email, and telephone number
Contact information

Dr. Paal Engelstad, Professor, tel: +47 67 23 85 38Ring: +47 67 23 85 38, email: paal.engelstad(at)


Salary is set in accordance with the Norwegian State Salary Scale, position code 1017 PhD Candidate, wage scale 50 ± 54 (429 700 ± 458 800 NOK).

The position adheres to the Norwegian Government’s policy that the national labour force should to the greatest possible extent reflect the diversity of the population. Therefore, we encourage qualified candidates with immigrant background or reduced functional ability to apply for this position. HiOA is an IA (Inclusive Workplace) enterprise and operates in compliance with the Norwegian IA agreement. 16/03266

Type of employment
Temporary position
Contract type
Full time
First day of employment
Fall 2016
429 700 ± 458 800 NOK
Employment expires
Number of positions
Working hours
Reference number
Last application date
09.Jun.2016 11:59 PM CET