Causal conclusions are at the center of research, yet notoriously difficult to obtain. In this project we aim to go beyond correlations and develop new statistical methods to draw reliable causal conclusions from high-dimensional data. This PhD position is part of the ERC-funded project 'BayCause: high-dimensional nonparametric Bayesian causal inference'.
You will do four years of research in causal inference, culminating in a PhD thesis. The focus of the PhD trajectory is on developing new methods and theory. In this subproject of the larger ERC-project 'BayCause', you will take the graphical approach rooted in directed acyclic graphs (DAGs). We will work in a high-dimensional setting, meaning that there are many covariates / features relative to the amount of individuals for which we have data ('small n, large p'). You will investigate settings in which the DAG is partially or fully unknown, and will research whether Bayesian methods are suitable for reliable causal effect estimation, including appropriate uncertainty quantification. We will consider various outcomes, including continuous, binary and survival outcomes.
This PhD position is jointly supervised by dr. Stéphanie van der Pas (Amsterdam UMC, Epidemiology and Data Science) and dr. Sara Magliacane (UvA, Amsterdam Machine Learning Lab).
Your main task will be to do research. You are expected to present your research at national and international workshops and conferences. Additionally, we expect you to participate in the Amsterdam UMC's teaching duties and departmental events.
We are looking for a motivated PhD student, with the following qualifications:
Please use the motivation letter to explain to what extent you meet these requirements.
In your application, please make sure to include the following:
Please combine the motivation letter, transcript and writing sample into one pdf.
We offer a fully funded 4-year PhD position, including a travel budget to attend workshops and conferences. The contract will be initially for 12 months, and will be extended by another 36 months in case of a positive evaluation.
You will join the vibrant Big Statistics research group, which is part of the Epidemiology and Data Science department at the Amsterdam UMC. You can read more about the group on Big Statistics.
Your supervisors will be dr. Stéphanie van der Pas (Stephanie van der Pas) and dr. Sara Magliacane (Sara Magliacane).
We currently work one day a week from the AMC location and one day a week from the VUmc location, with the option to work the remaining three days from home or from the AMC location.
Our preferred starting date is September 1st 2023. A start date between August 1st - October 1st 2023 can be discussed.
The first round of interviews is scheduled for May 2nd and May 4th. We will send out invitations for the first interview round by April 14th. A second round of interviews is tentatively scheduled for May 16th.
For questions about the position, you can contact dr. Stéphanie van der Pas, assistant professor, via email@example.com, or dr. Sara Magliacane, assistant professor, via firstname.lastname@example.org
For more information about the application procedure, you can contact Chey Edwards, corporate recruiter, via email@example.com or via 06-21487245.
A reference check and screening may be part of the procedure. Read here what this entails. If you come to work for us, we ask for a VOG (Certificate of Good Conduct).
Internal candidates have priority over external candidates in case of equal suitability.
Acquisition in response to this vacancy is not appreciated.