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In applications such as image processing and materials science, we wish to understand a complex system from noisy observations. This is the challenging setting of inverse problems. The PhD project concerns employing a Bayesian approach to such inverse problems with general, non-Gaussian priors, with the aim of preserving structure available in the information. Examples of non-Gaussian priors are Besov priors which
allow function discontinuities (similar in spirit to the TV regularization used in image processing). Additional challenges may be how to deal with situations where the likelihood is not differentiable or the likelihood computation is of black-box type and/or has no gradient information available.
One of the main challenges you will address is how to design suitable (Monte Carlo) algorithms for carrying out computations for such models. After first performing an exploration of the existing methods and literature available on the topic, the project may focus more on theoretical or applied aspects, depending also on the interests of the student.
This project is part of the Delft AI Lab ‘SLIMM-Lab’, a collaboration between Hanne Kekkonen and Joris Bierkens (Delft Institute of Applied Mathematics) and Iuri Rocha and Frans van der Meer (Civil Engineering and Geosciences). The goal of SLIMM Lab is to employ machine learning approaches towards efficient computation in materials science. The current PhD position focusses on some of the mathematical underpinnings
of these approaches.
Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.
TU Delft offers DAI-Lab PhD-candidates a 5-year contract (as opposed to the normal 4-years), with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2541 per month in the first year to € 3413 in the fifth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.
The TU Delft offers a customisable compensation package, discounts 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.
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!
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.
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