Are you a researcher with a passion for advanced machine learning techniques, data science, and Bayesian modelling and inference? Do you want to work in an interdisciplinary team, conducting research in a pleasant and open environment? As a postdoc, you will study AI technologies in relation to self-regulated learning. In this way, you generate insights in the type of hybrid support that is best suited for learning.

We are looking for a postdoctoral researcher with a background in Artificial Intelligence or a related field to work on the Hybrid Human-AI Regulation (HHAIR) project for 4 years (0.6 FTE) or 3 years (0.8 FTE) (depending on your capabilities, the position may be scaled up to 1 FTE with teaching duties).

Young learners (10-14 years) in today's society often use Adaptive Learning Technologies (ALTs) such as Gynzy, to optimise their learning experience of, for instance, mathematics and languages. However, existing ALTs take the control of the learning process away from the student. As a result, students have less opportunity to practise their self-regulated learning (SRL) skills. SRL involves students' control and monitoring of their own learning process, with metacognitive activities such as orientation, planning and evaluation.

In the Hybrid Human-AI Regulation (HHAIR) project, we gradually transfer ownership of the learning process from AI-regulated back to self-regulated learning, which we call hybrid learning. This way, young learners can benefit from an AI-assisted learning programme, while also developing their SRL skills. Depending on their SRL skills, the ALT will adapt to their developing needs. As such, HHAIR facilitates optimal learning of skills. Based on the ALTs trace data, we will determine the level of SRL a learner shows and the amount of AI guidance needed. The project is the first to explicitly combine SRL with AI-supported learning.

As a postdoctoral researcher, you will be part of the ALL team that investigates the ALTs trace data from daily usage in schools across Europe provided by the AI@EDU infrastructure. This process starts with an exploratory step (WP1). Here, we use Bayesian nonparametric modelling to characterise different types of learners and their prototypical behaviour. Subsequently, the insights from WP1 are incorporated into design studies. We use the results from the Bayesian model to predict which type of hybrid support is best suited for a student's learning (WP2). Together with our team, you will be responsible for the development and implementation of these algorithms.

The HHAIR team consists of Dr Inge Molenaar (Educational Science), Dr Max Hinne (Artificial Intelligence), Prof. Eliane Segers (Educational Science), two PhD candidates and one postdoctoral researcher.

The full project description is available upon request.

• You hold a PhD degree in a relevant field, e.g. artificial intelligence, machine learning, computer science, or similar.
• You have demonstrable experience with implementing advanced machine learning techniques.
• You have affinity with Bayesian nonparametric modelling and Bayesian approximate inference.
• You have strong mathematical skills.
• You have ample experience with writing academic publications.
• You are interested in putting your skills to use in the context of self-regulated learning and education in general.
• You have the desire to work in an interdisciplinary team.
• You have an excellent command of written and spoken English.

Fixed-term contract: 4 years 0.6 FTE / 3 years 0.8 FTE.

• An employment for 0.6 - 0.8 FTE.
• The gross monthly salary amounts to a minimum of €3,974 and a maximum of €5,439 based on a 38-hour working week, depending on previous education and number of years of relevant work experience (salary scale 11).
• You will receive 8% holiday allowance and 8.3% end-of-year bonus.
• It concerns a temporary employment for 3 years (0.8 FTE) or 4 years (0.6 FTE).
• You will be able to use our Dual Career and Family Care Services. Our Dual Career and Family Care Officer can assist you with family-related support, help your partner or spouse prepare for the local labour market, provide customized support in their search for employment  and help your family settle in Nijmegen.
• Working for us means getting extra days off. In case of full-time employment, you can choose between 29 or 41days of annual leave instead of the legally allotted 20.

Work and science require good employment practices. This is reflected in Radboud University's primary and secondary employment conditions. You can make arrangements for the best possible work-life balance with flexible working hours, various leave arrangements and working from home. You are also able to compose part of your employment conditions yourself, for example, exchange income for extra leave days and receive a reimbursement for your sports subscription. And of course, we offer a good pension plan. You are given plenty of room and responsibility to develop your talents and realise your ambitions. Therefore, we provide various training and development schemes.

The Faculty of Social Sciences is one of the largest faculties at Radboud University (Nijmegen, Netherlands). The faculty currently employs approximately 650 employees. The faculty's ambition is to become one of the top social science institutes in Europe, providing high-quality research and study programmes that rank among the best in the Netherlands. The Behavioural Science Institute (BSI) is part of the Faculty of Social Sciences. BSI is a multidisciplinary behavioural research institute in which researchers collaborate across the boundaries of psychology, educational science and communication science. It has seven research programmes covering three main research themes: (1) development and learning, (2) psychopathology, health and well-being, and 3) social processes and communication. BSI conducts applied/translational research as well as fundamental research. The BSI Graduate School (recognised by the Netherlands Organisation for Scientific Research) is responsible for the training of PhD candidates. BSI has state-of-the-art research facilities for observational studies, experiments, eye-tracking studies, EEG measurements and GSR recording, psychobiological research, and behavioural measurements in both real and 3D virtual environments. You will join BSI's Learning and Plasticity group. This programme deals with the micro-analysis of learning processes. The project is situated in the Adaptive Learning Lab (ALL) which is a technologically intensive research lab that is recognised as a pioneer in developing innovative learning technologies applying Learning Analytics and Artificial Intelligence techniques. ALL currently consists of seven PhD candidates, three postdoctoral researchers and two junior researchers, and is led by Associate Professor Inge Molenaar. ALL conducts research in different educational contexts with projects in primary education, secondary education and higher education. Moreover, ALL collaborates with a number of international partners in the EARLI Centre of Innovative Research (E-CIR). We offer a highly innovative context at an advanced research university with ample opportunities to travel, publish and develop yourself over the course of the project.

Radboud University
We want to get the best out of science, others and ourselves. Why? Because this is what the world around us desperately needs. Leading research and education make an indispensable contribution to a healthy, free world with equal opportunities for all. This is what unites the more than 24,000 students and 5,600 employees at Radboud University. And this requires even more talent, collaboration and lifelong learning. You have a part to play!