Deep learning is entering a new era. More fundamental questions are being raised comparing human-level performance with that of AI-powered machines. Current deep learning systems are data-hungry and require substantial supervision rendering them inefficient in many practical circumstances. They also illustrate significant performance degradation under data domain changes. In contrast, humans learn fast from only a handful of samples under weak or no supervision at all. Self-supervised learning sits at the core of a new learning paradigm that can potentially enable machines to learn meaningful abstractions of the world, allowing them to act more swiftly and responsibly.

In this PhD project, you will be conducting research and developing self-supervised learning algorithms applied to domain adaptation and zero/few-shot learning in the context of deep learning and computer vision. We are interested in both theoretical and applied research especially in the realm of remote sensing and satellite imagery data where redundancy and abundance of data gives rise to learning meaningful self-supervised representations. What makes this PhD project even more exciting is its connection with real-life industrial applications as well as a designated industrial internship opportunity given the dual affiliation of Dr. H. Jamali-Rad.

The project will be carried out in the research group of Dr. H. Jamali-Rad within the Computer Vision Lab (CV Lab) led by Dr. J. van Gemert, in collaboration with researchers at the faculty of EEMCS, the intelligent systems (INSY) department and the Pattern Recognition Lab (PRB Lab) at TU Delft. You will be offered flexibility in exploring and substantiating your research direction, hence candidates eager to learn and grow as scientific researchers are preferred. You will also be collaborating closely with other senior PhD researchers in the group and will supervise master and bachelor theses.

We are looking for candidates with the following criteria:

• A Master’s degree in Computer Science, Artificial Intelligence, Electrical Engineering, Applied Mathematics, or other fields closely related to the research topic;
• A strong mathematical background in statistical learning and optimization theory.
• A solid understanding in one of the following areas: deep learning and computer vision.
• A background in graph neural networks is a plus, but not mandatory;
• Experience in programming in Python and hands-on experience in building deep learning pipelines in Pytorch (or Tensorflow);
• A good command of English (written and oral);
• Ability to work independently and in a team and a deadline-oriented attitude.

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.

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2541 per month in the first year to € 3247 in the fourth 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 Adviceto assist you with your relocation.

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 valuesand we actively engageto 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 disciplines - electrical engineering, mathematics and computer science. Combined, they reinforce each other and are the driving force behind the technology we use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make future-proof. We are also working on a world in which humans and computers reinforce each other. We are mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. There is plenty of room here for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1,100 employees and 4,000 students work and study in this innovative environment.

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