PhD Research Fellow in Customer Behaviors Analytics and Prediction

Deadline: 30.06.2022

Western Norway University of Applied Sciences

With about 16,000 students, Western Norway University of Applied Sciences is one of the largest higher education institutions in Norway. A broad range of academic programmes are offered at Bachelor, Master and PhD levels, spread out on five campuses Førde, Sogndal, Bergen, Stord og Haugesund.

Our ambition is to build stronger and more solid academic and research environments that will interact nationally and internationally. The aim is to become a recognized actor on the international higher education arena. Increased international cooperation and engagement in externally funded projects will work towards this goal.

The Faculty of Engineering and Science has approximately 300 employees and approximately 3,200 students. The faculty has a broad educational offer at both bachelor's and master's level in engineering and science, as well as PhD education in computer technology. The Mohns Center for Innovation and Regional Development researches innovation and offers master's education in innovation and entrepreneurship. The diving education offers a one-year vocational school education.

The main part of the faculty's activities are in Haugesund, Bergen, Sogndal and Førde, but we also offer decentralized education in Florø, Kristiansund and Stord.

The faculty's activities are internationally based and take place in close collaboration with regional companies, clusters, health trusts and the public sector, including other institutions in the university and college sector. This applies to research, development, innovation and not least education with student projects at all levels.

Western Norway University of Applied Sciences, Faculty of Engineering and Science, has a full time (100%) position vacancy for a research fellow (PhD position) in Customer Behaviors Analytics and Predictionfor a period of 4 years.

The position is lineked to The Department of Computer Science, Electrical Engineering and Mathematical Sciences. The place of employment is Campus Bergen.

Research environment

The computer science research environment at Western Norway University of Applied Sciences has a strong focus on use-inspired and applied research, and on ICT as an enabling technology. The research environment has cooperation with many national and international research groups, and with national and regional industry partners. The research programme includes the research themes of software engineering, engineering computing, sensor networks and measurement technologies, grid computing and physics data analysis, machine learning, and interactive and collaborative systems. The prospective PhD candidate will work in close cooperation with our current PhD students in the intersection of ML/DL, optimization, and sequence data analytics. PhD research fellows receive an annual work expense funding which can be used for conference participation, research visits, and equipment.

About the PhD project/ work tasks:

Recently, online commerce has grown at an astronomical pace especially during the current pandemic situations, people intend to do the online shopping much more than the past years. However, many studies show that one-third of all online orders result in returns each year. Returns can present significant operational and logistical challenges, as (1) companies must expend resources such as staff and space to process returns, determine whether to resell or dispose of an item. In addition, if the returned items need to be repaired, this can (2) negatively impact workflows in the production process, depending on the product type and workload. From a financial perspective, return volume helps estimate the cost or loss due to returns. So, from a financial and operational perspective, understanding return behaviors is not only beneficial to the company, but to any company facing a high volume of returns in the age of e-commerce. In addition, high return rates, on the other hand, reduce profit margins due to generous return policies. E-commerce companies struggle to keep up with rising return costs such (i.e., shipping, restocking, and refitting), as well as other indirect costs (i.e., contact center demand and customer satisfaction). Predicting consumers' return behavior as they search for items or assemble their shopping carts has great economic significance and helps avoid unpleasant transactions. Historical records of product purchases and returns, on the other hand, provide a wealth of information but somehow, it is hard and difficult to optimize the features for predicting product returns.

The candidate of this position must hold at least tome of the following background knowledge and have good and relevant publications.

The required background for this position is as follows:

1. AI techniques in machine learning/deep learning

2. Evolutionary computation and optimization

3. Natural Language Processing (NLP)

4. Data mining and analytics in heterogenous, large-scale and big data

5. Collaborative recommendation system

6. Data security and privacy preservation

The candidate is also expected to integrate all the developed algorithms in a system or platform, to facilitate their use in other applications. The candidate will collaborate with a real industry (3Soft IT in Poland) to address the limitation of product returns.


The PhD research fellow must hold a master's degree in computer science or in a closely related field. Candidates who have submitted a master’s thesis (but who has not yet been awarded a master’s degree) may also qualify for the position provided that the master`s degree is awarded within 4 weeks after the application deadline. A solid background in ML/DL, data analytics and optimization will be considered an advantage when candidates are ranked. Candidates already holding a PhD within this field are not eligible for this position.

In addition to the required educational and scientific background, the following criteria will be evaluated: competence and grades on completed course work, quality of the master's thesis (excellent grade, equivalent of grade B or better on the ECTS grading system), publications (if any), research and teaching experience, practical software engineering skills and experience. A possible outline of a research plan for a potential PhD project will also be taken into account.

The candidate must be diligent and display the ability to work independently, supplemented with regular guidance, and is expected to carry out high-quality research and to publish the results in international workshops, conferences, and journals.

The PhD research fellow must enroll in the PhD programme in Computer Science: Software Engineering, Sensor Networks and Engineering Computing at Western Norway University of Applied Sciences, and must meet the formal admission requirements for admission into the PhD programme. 25% of the 4-year period will be designated to duties such as teaching, development and administrative tasks. The employment period may be reduced if the successful applicant has held previous employment as a research fellow.

The PhD candidate will be assigned two academic supervisor(s) at Western Norway University of Applied Sciences. An application for enrolment should first be submitted after an appointment is made and the supervisor(s) will help with this procedure. The candidate must be enrolled as a PhD student within 3 months from the start of the employment.

Application procedure:

Applications will be evaluated by an expert panel of three members.

Applicants are asked to submit their application and CV online. Please use the link “Apply for this job” (“Søk stillingen”).

The following documentation should be uploaded as an attachment to the online application:

  • Master thesis
  • Copies of selected academic publications (no more than 15)
  • A CV with a complete list of academic publications
  • Diplomas and certificates
  • Github repository (if any)

Applicants should indicate which publications or parts of publications should be given special consideration in the evaluation. If the documents submitted are not in a Scandinavian language or in English, the applicants must submit certified translations of these. The transcripts must specify the topics, the course works, and the grades at the bachelor`s and master`s degree levels.

Applicants should note that the evaluation will be based on the documentation submitted electronically via Jobbnorge within the submission deadline. The applicants are responsible for ensuring that all the documentation is submitted before the closing date. It is of utmost importance that all publications to be considered in the evaluation are uploaded as an attachment with the application, since these are sent electronically to the expert panel. Applications cannot be sent by e-mail or to individuals at HVL.


  • Good occupational pension, insurance and loan schemes from The Norwegian Public Service Pension Fund
  • Exciting academic environment with the possibility of competence enhancement and development
  • Opportunities for training within the working hours

Initial salaries will be offered at grade 54 (code 1017) in the Civil Service pay grade table scale. There is a compulsory 2 % deduction to the pension fund (see http://www.spk.no/en for more information). The successful applicant must comply with the guidelines that apply to the position at any time.

There is a compulsory 2 % deduction to the pension fund (see http://www.spk.no for more information). The successful applicant must comply with the guidelines that apply to the position at any time.

General information:

The appointment will be made in accordance with the regulations for State employees Law in Norway ("Lov om statens ansatte)". Organizational changes and changes in the duties and responsibilities associated with the position must be expected.

State employment shall reflect the multiplicity of the population at large to the highest possible degree. Western Norway University of Applied Sciences Bergen has therefore adopted a personnel policy objective to ensure that we achieve a balanced age and gender composition and the recruitment of persons of various ethnic backgrounds.

Information about the applicant may be made public even though the applicant has requested not to be named in the list of applicants. The applicant will be notified if his/her request is not respected.

Applicants may be called in for an interview.


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