You will work as part of a strong, collaborative research team on the development and application of AI-based inference methods for gravitational-wave cosmology, focusing on complex, high-dimensional LISA data.

A central element of the project is the use of simulation-based inference (SBI)—a rapidly developing alternative to classical likelihood-based data analysis enabled by recent advances in deep learning. Within the LISA context, these methods are developed in a complementary way to existing likelihood-based and pipeline-driven efforts aimed at achieving a global fit of the data, providing new tools to address LISA’s computational and statistical challenges.

In particular, you will work on sequential, hierarchical, and population-level inference methods for LISA data analysis. Your work will result in publications in leading peer-reviewed journals and presentations at international conferences. You will also contribute to open-source research software and participate in the supervision of Bachelor and Master students.

Tasks and responsibilities:

• Conducting independent research in gravitational-wave cosmology and data analysis, leading to publications in international peer-reviewed journals;
• Developing and applying AI-driven inference methods for LISA data;
• Contributing to open-source software for AI-driven gravitational-wave analysis;
• Presenting research results at international conferences and workshops;
• Assisting in the supervision of Bachelor and Master students, including co-supervision of theses and tutoring.

You are a highly motivated and self-driven candidate with a strong background in physics and a genuine interest in modern data-analysis, machine-learning, and AI-based statistical inference techniques. You enjoy working at the interface of theory, computation, and data analysis, and are keen to develop new AI-driven inference methods and implement them in scalable, high-performance research code, with the goal of extracting fundamental insights about the Universe from gravitational-wave data, including its cosmological and astroparticle-physics content.

Your experience and profile:

• A Master’s degree in physics, astronomy, or a closely related field;
• Strong analytical skills and a solid foundation in theoretical physics and cosmology;
• Experience with scientific programming and numerical methods;
• Prior experience with machine learning, deep learning, or simulation-based inference is a strong advantage;
• Fluency in spoken and written English;
• An excellent academic record, as evidenced by your curriculum vitae and grade transcript.

We offer a temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). The gross monthly salary, based on 38 hours per week ranges between € 3,059 to € 3,881 (Scale P). This does not include 8% holiday allowance and 8,3% year-end allowance. The UFO profile PhD candidate is applicable. A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants.

The preferred starting date is fall 2026. This employment should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students. Partially working from home is possible; regular on-site presence in Amsterdam is required.