PhD candidates in Methodology and Statistics

Do you want to contribute to extending, applying, and disseminating flexible methodologies from the Bayesian machine learning literature to various statistical problems for nonlinear social research problems?

Your position 

The PhD projects are part of an ERC Consolidator Project “NONLINEARSCIENCE” which will be supervised by Prof. Dr. Ir. Joris Mulder (Dept. of Methodology & Statistics) and colleagues. The PhD projects will focus on different topics such as

• Informative prior specification;
• Multivariate nonlinear modeling;
• Nonlinear relational event modeling of dynamics social networks.

Moreover, computational optimization and software development (R and JASP) are also important to allow researchers to use these techniques for their own research.

As a PhD candidate, your role will involve conducting research in the realm of Bayesian statistics. Regular meetings with the supervisor will be scheduled to review and discuss the project's advancement. The department fosters an open working culture, emphasizing mutual respect and appreciation among its members.

The project will be supervised by Prof. Dr. Ir. J. Mulder, Dr. U. Böhm and Prof. Dr. R. Leenders.

Your responsibilities  

• Your research work includes literature reviews, developing and extending statistical methodologies, analyzing theoretical & practical properties, analyzing empirical data, developing statistical software, and writing research reports.
• You will prepare scientific articles to be published in international journals, present key findings at national and international scientific conferences, and write a dissertation.
• You will actively participate in the Department of Methodology and Statistics.
• You will contribute to Open Science and Team Science. 
• You will contribute to education and supervision (e.g. supervising bachelor’s theses and research skills groups; not more than 10% of your time will be devoted)

Background

Many, if not all, real-world phenomena are nonlinear by nature. No mechanisms exist that can be described as a straight line having a fixed slope that goes on forever. In the social and behavioral sciences, nonlinearity can be observed in many empirical applications. Examples include the nonlinear integration process of new workers, the nonlinear temporal trajectories of well-being surrounding negative life events (e.g., unemployment, widowhood), or energy levels of students that progress in a nonlinear fashion (to name a few). To study such nonlinear phenomena, the challenge is to learn the entire nonlinear shape from the data rather than only learning the linear slopes as when using traditional (generalized) linear models. Moreover, prior information (e.g., based on experts’ knowledge) may be available that can inform us about plausible nonlinear shapes before observing the data. By combining these sources of information, we can have a more informed understanding about complex nonlinear social phenomena. For these statistical problems, Bayesian Gaussian processes will be used. The aim is to extend, apply, and disseminate this flexible methodology from the Bayesian machine learning literature to various statistical problems in social research.

Requirements:

We are looking for strong PhD candidates with a background in applied/mathematical/Bayesian statistics, machine learning, sociometrics (in particular social network modeling), econometrics, or the like, and a strong interest in nonlinear statistical modeling. Candidates with a social science background with very strong quantitative skills can also apply.

Other requirements include:

• A (nearly) completed (research) master’s thesis in the applied/mathematical/Bayesian statistics, machine learning, sociometrics (in particular social network modeling), econometrics or the like.
• Programming skills (e.g., R, C, Stan, or others) are a must.
• Research skills and data analytical abilities.
• Communication and cooperation skills and the willingness to work in a team.
• Project management and organization skills.Interest in open science and team science.
• Proficiency in English, including academic writing.
• Interest in providing small-scale education, such as teaching working groups or bachelor thesis supervision.

Salary Benefits:

Tilburg University offers excellent benefits in a pleasant working environment: 

• A position based on 1.0 fte (40 hours per week). 
• A salary of minimum €2.901 and maximum €3.707 gross per month for full-time employment, based on UFO profile PhD and salary scale PhD. Tilburg University uses a neutral remuneration system based on relevant work experience. 
• This is a vacancy for for a PhD in accordance with Article 2.3 paragraph 8 sub b CLA DU. You will initially be given a temporary contract for the duration of 12 months.
• Vacation pay of 8% and a year-end bonus of 8.3%. 
• Over 8 weeks of vacation leave. 
• The opportunity to work partly on campus and partly from home with a home office allowance of €2 per day.
• Reimbursement for sustainable commuting: walking, cycling, and public transport. 
• A monthly internet allowance of €25.
• An options model in which you exchange benefits for things such as additional leave, more pension, a bicycle or personal training at our Sports Center. 
• A moving allowance (subject to conditions). 
• Employees from abroad may be eligible for a tax-free allowance for extraterritorial expenses equal to 30% of taxable salary. 
• A pension with ABP; the largest Dutch pension fund.
• Training in personal development, career development, leadership, education, and research. Or a language course at our Language Center. 
• A work culture in which we embrace differences, everyone is welcome and given equal opportunities. 
• A vibrant campus in green surroundings that is easily accessible by public transport. 

For more information, see our website and the CLA Dutch Universities. 

40 hours per week

Warandelaan 2