PhD Position in Financial and Insurance Mathematics 80 - 100 %

Join a Leading Educational Institution

The University of Zurich, the largest university in Switzerland, offers a wide range of attractive career opportunities across various disciplines. With a dedicated workforce of around 10,000 employees and 12 professional apprenticeship streams, we provide an inspiring work environment dedicated to cutting-edge research and top-tier education.

Your Responsibilities

The focus of the doctoral thesis will center on nonlinear pricing and model risk in finance and insurance. Potential areas of research include:

• Mathematical analysis of pricing and risk measurement under uncertainty
• Robust and nonlinear valuation methods
• Contagion models
• Application of machine learning techniques in financial and actuarial modeling

We seek a highly motivated and talented individual with a robust background in mathematics, particularly in probability theory and stochastic analysis, alongside mathematical finance. The ideal candidate will be keen to develop rigorous mathematical methods while remaining open to applications in finance, insurance, and data-driven modeling.

This position is funded for a duration of four years, with the earliest start date in September 2026.

Your Profile

Ideal candidates will possess:

• A Master's degree, or equivalent qualification, in stochastic analysis, mathematical finance, or a closely related field
• A solid background in probability theory, stochastic processes, stochastic analysis, or financial mathematics
• An interest in financial and insurance applications
• A willingness to explore the interface between mathematics, finance, insurance, and machine learning
• Strong analytical skills and a passion for independent research

Research Environment

As part of the Chair of Quantitative Risk Analysis, the candidate will engage in a stimulating research environment that fosters collaboration within DM3L and the Zurich Graduate School of Mathematics. This role presents an opportunity to tackle mathematically rigorous and practically relevant challenges in financial and insurance mathematics.