Postdoctoral Fellow in Causal Machine Learning

at  Universiteit Gent

APPLY BEFORE 01/09/2024 (DD/MM/YYYY) 23:59 (BRUSSELS TIME)

• Faculty of Sciences
• Department: WE02 - Toegepaste Wiskunde, Informatica en Statistiek
• Occupancy rate:100%
• Number of positions: 1
• Type of employment: Contract of unlimited duration with clause
• Term of assignment: 4 jaar
• Wage scale: PD1 to PD4 (doctoral degree)
• Required diploma:PhD

ABOUT GHENT UNIVERSITY

Ghent University is a world of its own. Employing more than 15,000 people, it is actively involved in education and research, management and administration, as well as technical and social service provision on a daily basis. It is one of the largest, most exciting employers in the area and offers great career opportunities. With its 11 faculties and more than 80 departments offering state-of-the-art study programmes grounded in research in a wide range of academic fields, Ghent University is a logical choice for its staff and students.
The Causal Inference research lab at Ghent University is seeking a highly motivated and talented postdoctoral fellow to join its team. Ghent University has a long tradition of research in causal inference since the mid 90’s, and now has a vibrant causal inference community comprising over 20 statisticians dedicated to advancing this field.
This postdoctoral position is one of multiple positions being opened in connection to Advanced ERC Grant ACME ‘Assumption-lean (Causal) Modeling and Estimation’. In an era where the focus on causal inference is increasingly turning away from modeling towards quantifying population-level intervention effects, there is a risk of oversimplifying causal queries and of neglecting the rich history and efficacy of statistical modeling techniques. This ERC project aims to bridge this gap by leveraging the flexibility and power of statistical models to accurately represent intervention effects or facets of the causal data-generating mechanism, integrating it with recent insights from debiased machine learning and causal inference. Besides laying foundations for a novel paradigm for causal/statistical modeling, this project seeks to enhance the robustness and efficiency of debiased machine learning methods. This postdoc project will primarily focus on this latter component and on the development of techniques for orthogonal statistical learning, in interaction with fellow researchers on this project.

• At least 70% of your assignment will be spent on academic research.
• Studying, implementing, developing and improving state-of-the-art techniques for debiased machine learning and orthogonal statistical learning, to enhance their robustness and statistical efficiency.
• Using techniques from asymptotic statistics and empirical process theory, along with Monte Carlo simulation studies, to examine the large and finite-sample properties of the developed estimators.
• Applying or supervising application of the developed techniques for debiased machine learning or orthogonal statistical learning in substantive case studies with real world medical data.
• Collaborating with fellow researchers to develop foundations for a generic paradigm for assumption-lean causal/statistical modeling.
• Mentoring PhD students who collaborate on the above subjects.
• Writing high quality publications, targeting top journals and international conferences.
• In addition to your primary research responsibilities, you will actively contribute to the educational mission of our institution by providing (limited) support for courses in (mathematical) statistics. In addition, you take on a mentoring role by supervising bachelor or master theses related to the subject of this project.