The PhD student will work on machine learning in mobile media analytics to advance understanding of the relationship between adolescents’ smartphone use and their wellbeing.

Project and Profile

There are concerns that smartphone use poses risks for adolescent health and wellbeing and the goal of this project is to understand these risks using dynamic data collection techniques, such as smartphone logging and mobile experience sampling, and by developing algorithms that use this data to predict dynamic measures of mental health, well-being, and educational outcomes. The PhD student will explore state-of-the-art data mining and machine learning approaches and collaborate with experts in mental health and mobile media usage. These algorithms will be used to identify specific individuals or patterns of behavior that lead to elevated risk profiles. From a broader perspective, this project aims to increase the overall understanding of how mobile phone usage impacts adolescent well-being. It will be a flagship project in the developing field of computational social sciences research.

The PhD thesis is funded by the Advancing Society: Impact program of Tilburg University, under the theme: Health and Wellbeing. The subject of the thesis is expected to be an interdisciplinary research, which combines the following disciplines:

• Data Science – Predictive Modeling and Data Mining;
• Psychology – Developmental Psychology; 
• Communication Sciences – Media Effects Studies.

The research will be conducted under the supervision of Dr. Andrew Hendrickson, Dr. Mariek Vanden Abeele, Dr. Loes Keijsers, and Prof. Dr. Eric Postma at Tilburg University. It will deliver both long-term results (a better understanding of machine learning techniques for mobile media data) and mid-term results (algorithms, papers, and best practices).

The successful candidate is expected to:

• Perform scientific research in the domains described;
• Present results at (international) conferences;
• Publish results in scientific journals;
• Develop software that implements the algorithms described;

Candidates should:

• Have a Master’s degree in Cognitive Science, Psychology, Artificial Intelligence, Data Science, Mathematics, Statistics, Computer Science, or a related discipline;
• Have experience or a strong interest in the application of machine learning and data mining techniques in social sciences, particularly the study of new media and health;
• Have excellent programming skills (preferably Python, R, or C++);
• Be interested in interdisciplinary collaboration;
• Be a fast learner, autonomous and creative, show dedication and be hard working;
• Possess good communication capabilities and be an efficient team worker;
• Be fluent in English, both spoken and written. Additional proficiency in Dutch is preferred, but not necessary.

Fixed-term contract: 1 + 3 years.

The PhD student will be employed at Tilburg University and includes:

• A full-time position (38 hours per week).
• A minimum gross salary of € 2.266,- per month up to a maximum of € 2.897,-., in the fourth year;
• A holiday allowance of 8% and an end-of-year bonus of 8.3% (annually);
• Researchers from outside the Netherlands may qualify for a tax-free allowance equal to 30% of their taxable salary (the 30% tax regulation). The University will apply for such an allowance on their behalf;
• Assistance in finding accommodation (for foreign employees);
• The opportunity to perform cutting edge research in an interdisciplinary team bringing together expertise of several senior researchers;
• Support for your personal development and career planning including participation in courses, summer schools, conference visits, research visits to other institutes (both academic and industrial), etc.;
• A broad package of fringe benefits (including excellent technical infrastructure, savings schemes and excellent sport facilities). The collective labor agreement of Tilburg University applies to this position.
• The selected candidate will start with a contract for one year, concluded by an evaluation after approximately 10 months. Upon a positive outcome of the first-year evaluation, the candidate will be offered an employment contract for the remaining three years.