Vocational choice tests, in their current state, often fall short in delivering personalized and engaging recommendations, resulting in a lack of support during crucial choice moments at school. As a result, students rely heavily on parental advice, potentially limiting later career satisfaction. This research project aims to harness advanced machine learning techniques, such as (deep) reinforcement learning (RL), to offer tailored and improved vocational recommendations considering the individual student’s interests.

Recommender systems can be used to match specific vocations or career options to the user, based on the user profile and the characteristics of the vocational choice options. Leveraging the ability of deep learning (DL), deep reinforcement learning (DRL) can be applied in large action spaces, such as vocational choice tests. On the downside, because they use DL, deep RL algorithms and systems are less explainable than conventional RL systems, thereby potentially creating a black-box system.

The primary objective of this PhD project is to design and develop recommender systems that employ innovative algorithms to deliver personalized vocational guidance. By incorporating sophisticated machine learning, we aspire to create a user-centric approach that considers individual interests and preferences. Resulting matches have to be explainable to the user. Therefore, in this PhD project the black-box of DRL algorithms will be opened, for example through simultaneous explanation and policy learning.

Moreover, we aim to integrate feedback mechanisms that allow users to shape and refine their recommendations further. This innovative approach not only enhances the usability of vocational tests but also empowers individuals to make better career choices.

The main research goals are:

1. Designing and implementing explainable (deep) reinforcement learning algorithms for recommender systems.
2. Evaluating algorithm performance through (1) user opinions on the recommendations, (2) user opinions on the vocational choice test itself, and (3) long term satisfaction and relevance of recommendations (validity).

We look for a highly motivated, enthusiastic researcher who is driven by curiosity and has/is:

• Master’s degree or equivalent in Psychometrics, Statistics, Data Science, Artificial Intelligence or a related field;
• Affinity and/or experience research in the social sciences (and conducting research involving human participants);
• (Strong) programming skills with experience in machine learning and reinforcement learning being a plus.
• A good team spirit and like to work in an interdisciplinary and internationally oriented environment;
• Able to conduct independent research and willing to develop writing and publication skills;
• Possess good communication skills and an excellent command of English.

We encourage high responsibility and independence, while collaborating with colleagues, researchers, other university staff and partners. We follow the terms of employment by the Dutch Collective Labour Agreement for Universities (CAO). Our offer contains: a fulltime 4-year PhD position with a qualifier in the first year; excellent mentorship in a stimulating research environment with excellent facilities; and a personal development program within the Twente Graduate School. It also includes:

• Gross monthly salary of € 2.770 in the first year, increasing each year up to € 3.539 in the fourth year;
• Excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
• 29 holidays per year in case of full-time employment;
• A training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
• A green campus with free access to sports facilities and an international scientific community;
• A family-friendly institution that offers parental leave (both paid and unpaid);
• A full status as an employee at the UT, including pension, health care benefits and good secondary conditions are part of our collective labour agreement CAO-NU for Dutch universities.

The CoDE section consists of teachers and researchers with backgrounds in psychology, educational science, mathematics, or computer science. We teach courses on methodology, data-analysis, and cognitive psychology. Our research focuses on data-based solutions for societal problems related to education, health, and human factors. In our teaching and research we make use of the latest developments in measurement and (large-scale) assessment, such as neurophysiology, process data, extended reality, eye-tracking, psychometrics, and machine learning.

CoDE is part of the Department Learning, Data Analytics and Technology (LDT).