PhD Position Topological Signal Processing and Learning
2 maanden geleden
The processing of data with irregular and complex structures is crucial for advancements in networked engineering and natural systems. Traditional graph-based methods have matured over the last decade, providing robust frameworks in graph signal processing and graph machine learning. However, graphs are inherently limited to pairwise relationships and are primarily suited for data over nodes. Many real-world data sets exhibit multiway relationships that graphs cannot adequately represent. For instance, data related to flows are better modeled on edges (pairs of nodes) rather than on nodes. To address these complexities, topological tools such as simplicial and cell complexes offer a more fitting representation of the data domain.
You will be part of a dynamic and interdisciplinary research group led by Dr. Elvin Isufi at TU Delft. Dr. Isufi’s group has a strong track record in graph signal processing and graph machine learning, focusing on both theoretical and applied research. This includes applications in recommender systems within the Multimedia Computing Group and water networks in collaboration with AidroLab.
The group fosters a collaborative and stimulating research environment, providing ample opportunities for professional growth and innovation. You will work closely with experts in topological data analysis, signal processing, and machine learning, as well as collaborate with civil engineering researchers from AidroLab. The position is based at our cutting-edge research facility, equipped with the latest tools and resources to support your research endeavors.
The project offers high flexibility and research momentum within the group. Candidates who are eager to develop their own ideas within the project framework, collaborate with team members, and work independently are highly encouraged to apply. This project targets research output at the intersection of signal processing and machine learning, appealing to candidates with interests and expertise in both domains.
Additionally, PhD students at TU Delft are expected to spend around 10% of their time on teaching duties, which include preparing course material and supervising master’s and bachelor’s student theses. These teaching duties align well with the objectives of the project, providing a well-rounded academic experience.
If this sounds interesting, join our team in pushing the research boundaries for topological signal processing and learning and make an impact on both fundamental and applied research.
Requirements
We are looking for highly-motivated and ambitious candidates with the following criteria:
- A Master’s degree in Electrical Engineering, Computer Science, Applied Mathematics, or any field related to the research topic;
- A strong mathematical background in optimization, statistical learning, linear algebra and probability;
- A solid understanding of the fundamental aspects of machine learning and basic knowledge of deep learning
- An experience in programming in Python or related and in programming machine learning solutions 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 .
Conditions of employment
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 € 2770 per month in the first year to € 3539 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.
T he TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution . Flexible work schedules can be arranged.
For international applicants, TU Delft has the . This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a for partners and they organise events to expand your (social) network.
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 and we actively 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 to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.
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