PhD Studentship in Modeling and Estimation: Ghent University is offering two PhD studentships in the context of the Advanced ERC Grant ACME. This project focuses on enhancing causal inference through statistical modeling and debiased machine learning techniques, particularly applied to real-world medical data.
Designation:
PhD Student
Research Area:
- Causal Inference
- Statistical Modeling
- Debiased Machine Learning
Location:
Department of Mathematics, Computer Science and Statistics, Ghent University, Belgium
Eligibility/Qualification:
- Master’s degree in Statistics, Mathematics, Physics, Computer Science, Engineering (with a strong statistics or data science component), or an equivalent qualification.
- Excellent (‘honors’-level) academic grades.
- Strong background in statistical data analysis or machine learning.
- One candidate will specialize in machine learning, and the other in statistics and data analysis; familiarity with causal inference or debiased machine learning is an advantage.
- Creative and analytical skills, team-oriented mindset, and excellent communication abilities.
- Fluent in English (C1 CEFR level) for both speaking and writing.
Job Description:
The selected candidates will contribute to the development of debiased learning methods and assumption-lean modeling tools, applying these to real-world medical data while collaborating with a team of statisticians and machine learning researchers.
How to Apply:
Interested candidates should send their CV, transcripts of study results, a maximum one-page motivation letter outlining their relevance for the position and skills acquired, and at least two reference contacts to Stijn.Vansteelandt@ugent.be. Please use the subject line ‘Application: PhD ACME’. Note that the transcripts submitted do not need to be official at the time of application, but they must be official upon recruitment.
Last Date to Apply:
June 14, 2025, by 00:00.
This is an excellent opportunity for motivated students interested in advancing their careers in academia and research within a cutting-edge project at Ghent University.
