Graph signal processing (GSP) is an exciting research field with many applications. This project focuses on the non-trivial extension of GSP to time-varying or dynamic networks.
Job description
GSP is still in its infancy and we are yet to discover its full potential. This project takes a crucial step in this direction. Current GSP tools mostly consider the support of the signal (i.e., the graph) to be time-invariant or static. This is true when the data can be assumed stationary and the dimensions of the data do not change. The main innovation GraSPA introduces is to extend GSP tools to dynamic graphs, where we distinguish between two types of changes: changes in the connections (CC) due to for instance nonstationary data, and changes in the number of nodes (CN). The CC scenario occurs for instance in brain activity monitoring where the connections between different brain regions change over time depending on the performed task. The CN scenario is of importance in recommender systems where new users/items enter the system. For these two types of dynamic graphs, we propose contributions in two key directions in GSP. First, we will extend existing tools for graph topology identification to dynamic graphs. Such approaches will be data-driven and aim at revealing the hidden structure in large amounts of data (e.g., coming from brain activity monitoring and recommender systems). For the CC scenario it will be crucial to track how the connections between nodes change over time, whereas for the CN scenario we want to learn a model for the connections of the new nodes entering the graph. And second, we will propose graph filtering approaches with limited storage, data transfer, and computational needs to process the data (remove noise and outliers, highlight specific graph frequencies, or zoom in on certain localized features) as well as to predict missing or future values (e.g., ratings in a recommender system or future brain activity).
Job requirements
- A PhD degree in an engineering discipline relevant to the research
- Strong background in linear algebra, signal processing, detection an destimation, and optimization
- Background in graph signal processing is desirable
- Experience in programming e.g., Python, MATLAB, R
- Good verbal and written English skills
- Excellent communication and interpersonal skills
- Ability to work in a collaborative environment
TU Delft (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 of 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.
Conditions of employment
- Duration of contract is 1,5 years Temporary
- A job of 38 hours per week.
- Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
- An excellent pension scheme via the ABP.
- The possibility to compile an individual employment package every year.
- Discount with health insurers on supplemental packages.
- Flexible working week.
- Every year, 232 leave hours (at 38 hours). You can also sell or buy additional leave hours via the individual choice budget.
- Plenty of opportunities for education, training and courses.
- Partially paid parental leave
- Attention for working healthy and energetically with the vitality program.
Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands. .
Additional information
Application procedure
Are you interested in this vacancy? Please apply no later than17 February 2026
via the application button and upload the following documents:- CV
- Motivational letter
Please note:
- You can apply online. We will not process applications sent by email and/or post.
- As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on our website about knowledge security.