PhD candidate On the next generation of structural equation modelling tools

We are looking for a talented and ambitious PhD candidate in methodology and statistics as part of the NWO-funded VICI project “Towards personalized multi-disciplinary treatment plans: The next generation of structural equation modelling tools” (SEM2.0).

As a PhD candidate, your role will involve conducting research in the realm of multivariate 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. K. Van Deun, Dr. J. Jongerling and Dr. K. Loter.

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
Non-observable constructs such as personality, intelligence, and well-being are at the core of research on human behaviour and cognition. Latent variable methods (e.g., factor analysis, structural equation modelling) are therefore an indispensable tool for research in the social and behavioural sciences. These methods are known to work well when the number of parameters to estimate is relatively small compared to the sample size, that is, when the number of variables is small and the models are not too complex. However, two aspects of modern research practices challenge these conditions and cause existing methods to fall short: (1) a trend to use intensive collections of data (“Big Data”) and (2) the call for person-centric models.
The SEM2.0 project develops the next generation of structural equation models, suitable for working with highly complex data problems encountered in empirical research. It builds on a strong modelling framework that explicitly includes the latent variables and avoids the ill-conditioned problem of inverting covariance matrices. Furthermore, alternating optimization and parallel computing methods will be used to obtain powerful and scalable algorithms that have good convergence properties. This makes them effective for the analysis of (big) data. By adding regularizing constraints the method will account for simple structure, the presence of joint and specific latent variables in multidomain data, alignment in multigroup and longitudinal data, and the need for personalization of the measurement and/or structural model. Regularization is a powerful tool to add structure to the data analysis and to avoid overfitting in highly complex models.  In collaboration with health- and well-being researchers, large collections of data resulting from two empirical studies will be analyzed to develop methods and software that can and will be used by many researchers.
The PhD position is integral to a broader research initiative, which includes other PhD students and staff members.. This PhD project focuses on developing and validating the next generation of structural equation models for longitudinal data.

We are looking for a strong PhD candidate with a relevant (Research) Master’s degree with a background in applied/mathematical statistics, machine learning, sociometrics, econometrics, or the like, and a strong interest in computational statistics. Candidates with a social science degree with very strong quantitative skills can also apply. The degree should be completed or almost completed.

Other requirements include:

• A (nearly) completed (research) master’s thesis in the applied/mathematical/Bayesian statistics, machine learning, sociometrics,, econometrics or the like.
• Programming skills (e.g., R, Python, Matlab, or others) are a must.
• Research skills and data analytical abilities, including knowledge of computational statistics.
• 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.

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. 

Tilburg School of Social and Behavioral Sciences (TSB) is a modern, specialized university. The teaching and research of the Tilburg School of Social and Behavioral Sciences are organized around the themes of Adaptive societies, organizations, and workers, Healthy life span, Personalized prevention and care. The School's inspiring working environment challenges its workers to realize their ambitions; involvement and cooperation are essential to achieve this.

Tilburg School of Social and Behavioral Sciences

Recruitment code
Tilburg University applies the recruitmentcode of the Dutch Association for Personnel Management & Organization Development (NVP).

Disclaimer
The text of this vacancy advertisement is copyright-protected property of Tilburg University. Use, distribution and further disclosure of the advertisement without express permission from Tilburg University is not allowed, and this applies explicitly to use by recruitment and selection agencies which do not act directly on the instructions of Tilburg University. Responses resulting from recruitment by non-contractors of Tilburg Universities will not be handled.