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The PhD position is part of the project “Bayesian Gaussian Processes: Or How I Learned to Stop Worrying and Love Nonlinear Social Science”. The goal of this project is to develop statistical methods based on Bayesian Gaussian processes that allow us to combine different sources of information (prior and data) to study nonlinear phenomena. Gaussian processes are a flexible nonparametric (or infinitely parametric) approach from the field of (Bayesian) statistics and machine learning. Important topics that need to be addressed in this project include the formulation (and elicitation) of informative Gaussian process priors, the testing of statistical hypothesis (using Bayes factors) about nonlinear phenomena, and the feasibility of computational algorithms (among others). Moreover, the methods should be implemented in user-friendly statistical software to allow researchers to use these techniques in their own research. The project, which is funded by an ERC Consolidator Grant, will be supervised by Dr. Ir. Joris Mulder (Dept. of Methodology & Statistics).
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 processes 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.
The PhD position is integral to a broader research initiative, which includes other PhD students and staff members. As a PhD candidate, your role will involve conducting research in the realm of nonlinear statistical modeling using Bayesian Gaussian processes for applications in the social sciences. 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.
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 nonlinear statistical modeling. 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:
Increasing your value
With us, you will find everything you need to maximize your potential and development. We offer excellent facilities and support for research, education, and making societal impact. In all three of these areas, we “recognize and reward” you in line with national university aspirations. With great opportunities such as collaborating in an academic collaborative center, or participating in our Connected Leading program. We attach great importance to team spirit and have a clear, shared vision of (personal) leadership (Connected Leading). Read more about careers at Tilburg University and personal development here.
Your terms of employment
Your valuable contribution will be rewarded with attractive benefits and sufficient attention to work-life balance. Our offer includes:
In addition to your monthly salary, you will receive 41 vacation days (for a 40-hour work week), a holiday allowance (8%), and a year-end bonus (8.3%). We reimburse the full cost of sustainable commuting: walking, cycling, or public transportation. We have a moving expenses scheme that makes it attractive to live close to the university. You will be enrolled in the ABP pension fund through us. Our Options Model allows you to choose from a variety of facilities at a tax advantage. You can work in a hybrid manner: on campus and, for a reimbursement, from home. Researchers from outside the Netherlands may qualify for a tax-free allowance of 30% of their taxable salary if they meet the relevant conditions. The university applies for this allowance on their behalf.
Your work environment
You will work in a pleasant working environment; a green campus with plenty of facilities. At a leading, entrepreneurial, and innovative university in the humanities and social sciences. The university employs 2,400 staff members and hosts 20,000 students of some 100 different nationalities. For nearly a century, this organization has worked together with a tradition aimed at contributing to society. We strive to be a community where differences in age, gender, orientation, and cultural and religious backgrounds are valued, with equal opportunities for colleagues and students and where, moreover, all decisions take into account the importance of preserving the Earth for future generations.
Read more about the university here.
About Tilburg School of Social and Behavioral sciences
Tilburg School of Social and Behavioral Sciences (TSB) is one of the five faculties of Tilburg 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”, and “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 | Tilburg University
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