Substance (ab)use is one of the most common mental health problems in young adults in urbanized areas. There is a limited number of successful interventions, and their effectiveness is hampered by the complicated, multilevel dynamics of the associated risk factors and risk groups. In particular, current studies into risk factors do not take people's genetic vulnerability into account, which may lead to wrong conclusions. The goal of this project is to improve existing interventions by incorporating key risk factors and risk groups identified through genetically-informed designs.
As a PhD candidate you will work together in a multidisciplinary team and will focus on two key threads of research that will run parallel and inform each other:
- Genetic Epidemiological Research: You will perform genetic epidemiological studies using existing datasets to determine the most relevant (socio)environmental risk factors and at-risk groups for substance use. New insights from these analyses will be used to improve existing interventions.
- Improving current interventions for substance use. In addition, you will perform qualitative and quantitative analyses to detect problems with current interventions for substance (ab)use, and to improve them.
You will co-develop the research projects, analyze (large-scale) quantitative data and qualitative data, write up scientific manuscripts, present at conferences, and communicate with stakeholders. The five-year project will result in a thesis, consisting of multiple research articles. This project offers a unique opportunity to contribute to research in the field of mental health, with a focus on integrating genetic insights into intervention studies, that has the potential to have real societal impact. To help you excel in this role, we will provide ample opportunity for you to deepen and broaden your knowledge about the topic of substance use and mental health problems in general, offer support and training to master the analysis techniques involved in the project (genetic analyses, network analyses, intervention design and research).