Graph machine learning (Graph ML) is an emerging field of artificial intelligence (AI) motivated by the ubiquity of graph-structured data in real-world applications. Graph neural networks (GNNs) serve as a key technology in this area and are successfully used in recommendation systems, financial crime analysis, cybersecurity, and social and biological network analysis. However, existing GNN architectures are limited in the type and complexity of subgraph patterns they can detect in practice. For example, cycles, cliques, and frequent motifs can serve as highly discriminative signatures when analysing financial, biological, and social networks. However, cost-efficient discovery of such subgraph patterns using GNNs is challenging. Although these patterns can be recognized using purely combinatorial approaches, such approaches lack statistical learning and adaptation capabilities and also have high computational complexity.

A key objective of this project is to analyse the trade-offs between combinatorial and neural-network-based approaches in the Graph ML space. We aim to explore an unexplored design space, where GNNs and combinatorial algorithms can be combined based on linear-algebra-based primitives, leading to more efficient solutions. Our goal is to answer questions like the following: 

• What capabilities are needed in GNNs to enable the discovery of discriminative subgraphs?
• How can combinatorial subgraph discovery algorithms and GNNs strengthen each other?
• Can subgraph discovery be accelerated using linear algebra operations on GPUs and TPUs?

The project will cover some real-world applications of Graph ML in the financial space, including analysis of financial transaction networks to detect criminal activities such as money laundering. In this area, we will explore new ways of combining GNNs and Large Language Models (LLMs) to construct machine learning solutions that can operate on graph-structured data with multimodal node and edge features. Furthermore, because financial crimes can span multiple financial institutions and national borders, we aim to design and develop novel decentralized, secure, and privacy-preserving graph machine learning technologies that enable cooperation and collaboration in a distributed setting across financial institutions, regulatory authorities, and central banks.

We are looking for candidates who satisfy the following requirements:

• an MSc degree with excellent results in Computer Science and Mathematics, preferably in distributed systems, theory, machine learning, or related areas,
• a solid background in graph theory, algorithms, and complexity analysis,
• strong programming skills and practical experience with Python and C/C++,
• hands-on experience with Deep Neural Networks using PyTorch or TensorFlow,
• good speaking and writing skills in English with a minimum TOEFL-score of 100 or a minimum IELTS score of 7.0 per sub-skill (writing, reading, listening, speaking), which applies to all candidates wanting to pursue a PhD or PDEng programme at TU Delft.

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.

Fixed-term contract: 4 years.

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.

The 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 Coming to Delft Service. 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 Dual Career Programme for partners and they organise events to expand your (social) network.

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.

Delft University of Technology invites applicants for a PhD position in the Distributed Systems Group in the Department Software Technology of the Faculty of Electrical Engineering, Mathematics and Computer Science.

The Distributed Systems Group

The Distributed Systems group (http://www.ds.ewi.tudelft.nl), under the leadership of Dr. Lydia Y. Chen, performs world-class research in the design, implementation, deployment, and analysis of large-scale, Internet-based computer systems. It currently has three research lines: scheduling and resource management in distributed computing systems (e.g., in clusters and clouds), big-data analytics (e.g., differential approximate processing), and cooperative systems (blockchain technology, trust and reputation systems). Its research is fundamental, aimed at the development and evaluation of new generic concepts in systems software, and application-driven, motivated by important application areas. Much of it is experimental, validating the proposed new concepts by means of implementation and deployment in prototypes that are used in the real world.

The Department Software Technology

The Department of Software Technology (ST) is one of the leading Dutch departments in research and academic education in computer science, employing over 150 people. The department ST is responsible for a large part of the curriculum of the bachelor’s and master’s programmes in Computer Science as well as the master’s programme in Embedded Systems. The inspiration for its research topics is largely derived from technical ICT problems in industry and society related to large-scale distributed processing, embedded systems, programming productivity, and web-based information analysis.