Currently, education provides people with qualifications that do not always keep pace with the evolution of knowledge, skills and competences required. Similarly, it is largely unclear which skills are required for current jobs and future job openings. These information asymmetries result in labour market mismatch and ineffective job search, career planning and training activities.
 
As a first part of this PhD project, and to reduce existing information asymmetries effectively, the development of a unified online skills platform (“Skills Aligned”) linking people, jobs and training, is supported academically. This platform uniformly links acquired skills (people), required skills (vacancies/jobs) and potential skills (trainings/educational programs). Throughout this developmental process, scientific innovative web crawling algorithms for identifying skills, standardizing vacancies, probabilistic modelling techniques and deep learning methods for predictive analysis of complex network data are deployed and rigorously evaluated. 
 
The second part of this PhD trajectory acknowledges that the aforementioned techniques can further innovate adaptive algorithms currently developed in the fields of educational psychology, economics and computer sciences. One particular objective is to exploit the potential to offer students optimal personalized learning paths. New insights will be transformed into innovative evolutionary practicing and learning algorithms (e.g. machine learning, dynamic programming, bounds analyses, Bayesian statistical techniques) and empirically evaluated in real-life educational practice as to improve student learning and outcomes.

• A Master’s degree in computer science, artificial intelligence or adjacent area;
• Statistical modelling skills;
• Strong interest in education and learning;
• Competence in causal impact evaluations;
• Excellent ability to communicate in written and spoken English;
• Good social skills, ability to work independently, and strong scientific motivation.

The initial appointment will be for a period of 1 year. After satisfactory evaluation of the initial appointment, it will be extended for a total duration of 3 years. Information about our excellent fringe benefits of employment can be found at http://www.workingatvu.nl. 

The salary will be in accordance with university regulations for academic personnel, and ranges from a minimum of € 2.222 gross per month up to a maximum of € 2.840 gross per month based on full-time employment (salary scale 85) for a PhD student. 
 

Vrije Universiteit Amsterdam

Vrije Universiteit Amsterdam (VU) is a leading, innovative and growing university that is at the heart of society and actively contributes to new developments in teaching and research. Our university has ten faculties, and provides work for over 4,500 staff and scientific education for more than 23,000 students.

The Faculty of Behavioural and Movement Sciences (FBM) at VU Amsterdam is unique in the wide field of behavioural and health sciences for combining the disciplines of psychology, education theory and human movement sciences. The faculty contributes to a better understanding of the way behaviour and movement are caused and can be influenced so as to promote physical and mental health and improve performance.
 
The Amsterdam Center for Learning Analytics (ACLA) is an academic center with the objective to improve educational outcomes by combining insights from information technology, behavioural sciences, learning and education, and rigorous empirical evaluation methods. ACLA is part of the department educational sciences, section methods and statistics, in which this PhD project, investigating adaptive learning and skill formation, is offered.