The network approach to psychopathology defines mental illness as a system of dynamically interacting symptoms instead of a latent entity that causes a number of symptoms. The goal of this Gravitation Project is to take the network approach to psychopathology to the next level, and to critically test is value and usefulness for personalized treatment.

Background: The current project within this program of research constitutes the estimation of specific individual networks for a great variety of mentally ill patients. By studying the progression of individual networks over time, we will understand how symptoms affect each other within an individual over time. The theoretical model assumes that two individuals with the same clinical presentation (e.g., posttraumatic stress disorder) may be characterized by quite different network structures and therefore require a different focus in therapy. However, it may be unlikely that truly each and every individual with a certain clinical presentation has a totally different network structure. We will proceed to identify whether individual networks can be grouped based on shared network structures to study typical network profiles and to examine how they relate to traditional categorical diagnoses. Furthermore, we will focus on the variability and stability of individual networks within subjects, and the mediation and moderation by associated variables (social, context/location, time of day, stressors, medical/physical condition).

Challenges and tasks: In consultation with the consortium members and associated clinicians, a set of to-be-measured variables and acquisition methods needs to be developed. Important considerations that guide our data-collection are (a) the timing of measurements for an optimal tracking of processes in time, (b) the identification of symptom variables in an individual network, and (c) the inclusion of potentially relevant external variables. In addition, you will perform data-analysis and estimate individual networks in close collaboration with a postdoc and PhD student specialized in data science, who focus on optimization and further development of network data-analysis. Therefore, you will benefit directly from these methodological developments.

·         A (Research)Master’s degree in (clinical) psychology

·         A keen interest in and preferably experience in network methodology

·         Strong statistical skills

·         Programming skills, preferably R.

·         Well-developed analytical skills and creativity.

·         Excellent writing and presentation skills (English).

Fixed-term contract: 4 years.

We offer a (in total) 4 year full-time appointment as PhD candidate. At the end of the first year your performance will be reviewed and upon a positive assessment, your contract will be extended for a three-year period.

The terms of employment of Maastricht University are set out in the Collective Labour Agreement of Dutch Universities (CAO). Furthermore, local UM provisions also apply. For more information look at the website http://www.maastrichtuniversity.nl > Support > UM employees.

Maastricht University

Maastricht University is renowned for its unique, innovative, problem-based learning system, which is characterized by a small-scale and student-oriented approach. Research at UM is characterized by a multidisciplinary and thematic approach, and is concentrated in research institutes and schools. Maastricht University has around 18,000 students and 4,300 employees. Reflecting the university's strong international profile, a fair amount of both students and staff are from abroad. The university hosts 6 faculties: Faculty of Health, Medicine and Life Sciences, Faculty of Law, School of Business and Economics, Faculty of Science and Engineering, Faculty of Arts and Social Sciences, Faculty of Psychology and Neuroscience.