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Irène Gijbels

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1 week ago

PhD Position in Mathematical Statistics: Non-Euclidean Data Methodologies KU Leuven in Belgium

I am offering a PhD position in mathematical statistics at KU Leuven, focusing on non-Euclidean data methodologies.

Keywords

Computer Science
Mathematics
Risk Assessment
Python Programming
R Programming
Mathematical Statistics
Semiparametric Methods
Computational Statistics
High-dimensional Data
Statistics
Statistical Modelling
Applied Mathematic
Methodologie

Description

This PhD position at KU Leuven offers an exciting opportunity to join a vibrant research group specializing in mathematical statistics. The successful candidate will work within the ORSTAT group at the Faculty of Economics and Business and the Statistics and Risk section of the Department of Mathematics, Faculty of Science. The research environment is dynamic, international, and highly collaborative, providing access to a large network of experts and resources.

The project focuses on developing innovative statistical methodologies for data that reside in non-Euclidean spaces, such as circles, hyperspheres, and tori. These types of data are prevalent in various applications, including wind direction analysis and animal movement studies. The research aims to create flexible modelling and inference techniques, particularly semiparametric and nonparametric methods, for directional data. A key challenge is to accommodate both Euclidean and non-Euclidean data, often in high-dimensional settings, while avoiding overly restrictive assumptions.

Candidates should have a strong interest in the mathematical foundations of statistics and be motivated to develop new methodologies with solid theoretical support. Practical implementation of these methods is integral to the project, so skills in computational statistics and programming (especially in R or Python) are highly valued. The position is open to applicants with a Master's degree in Statistics, Mathematics, Applied Mathematics, Economics, Business Engineering, Civil Engineering, or Computer Science, provided they have achieved at least the grade of distinction. Final-year Master's students are also encouraged to apply. Proficiency in written and spoken English is required.

KU Leuven offers a high-level international research environment and a supportive team of supervisors, Professors Irène Gijbels and Gerda Claeskens, both recognized for their international reputation and expertise. Funding for the position is secured for four years, with annual contracts renewable up to a maximum of four years, contingent on positive evaluations.

To apply, candidates should submit a letter of motivation detailing their research interests and skills, a curriculum vitae, transcripts of grades for all university courses (including up-to-date grades for current Master's students), and the names and contact information of 2–3 references. Applications should be submitted via the provided online link. For further information, applicants may contact Prof. dr. Irène Gijbels or Prof. dr. Gerda Claeskens by email.

KU Leuven is committed to fostering an inclusive, respectful, and socially safe environment, embracing diversity and equal opportunity for all. The university does not tolerate discrimination of any kind and provides support for accessibility and other needs.

Funding

Available

How to apply

Submit your application online including a letter of motivation, curriculum vitae, transcript of grades for all university courses, and names/contact information of 2–3 references. Use the provided application link. For further information, contact Prof. dr. Irène Gijbels or Prof. dr. Gerda Claeskens via email.

Requirements

Applicants must hold a Master's degree in Statistics, Mathematics, Applied Mathematics, Economics, Business Engineering, Civil Engineering, or Computer Science, with at least the grade of distinction. Final-year Master's students may also apply. Candidates should demonstrate a clear interest in mathematical foundations of statistics and in developing new methodologies with theoretical support. Practical skills in computational implementation using open source software (such as R or Python) are advantageous. Proficiency in written and spoken English is required.

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