The project will focus on handling statistical inverse problems with non-Gaussian priors. Gaussian process priors are often used in practice due to their fast computational properties. However, the smoothness of the resulting estimates is not well suited for modelling functions with sharp changes, met for ecample in imaging. As an alternative for Gaussian priors one can consider Besov priors that are well suited for producing piecewise smooth reconstructions and for edge and interface detection.

The goal of the project is to analyse the statistical behaviour of the resulting posterior distributions and to design suitable algorithms for carrying out computations for these models. The project may focus more on theoretical or applied aspects, depending on the interests of the applicant. The project requires knowledge of functional analysis / numerical computation / inverse problems / Bayesian inference. No prior knowledge on Besov priors is required. 

• Doctors degree in mathematics or related field with specialisation on (Bayesian) inverse problems, optimisation, sampling methods, or machine learning.
• Affinity with coding numerical methods, e.g. in Matlab, Python, or Julia.
• English proficiency, both verbally and in writing. 

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities. The TU Delft offers a customisable compensation package, a discount on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged.

For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation. An International Children's Centre offers childcare and there is an international primary school.

Duration of the contract: 2 years.

Delft University of Technology

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Electrical Engineering, Mathematics and Computer Science

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.