PhD Position in Processing and Learning over Dynamic Graphs  

We are seeking for highly-skilled and self-motivated candidates with a strong mathematical background to do a Ph.D. on the fundamental aspects of signal processing and machine learning over dynamic graphs.

Graphs are playing an ever increasing role in nowadays systems as a flexible tool to model complex systems. In addition, these systems generate a vast amount of data which can be modelled as signals or features over these graphs. This is for instance the case of infrastructure networks such as water, energy and transportation networks but also the case of social, brain, and financial networks to name a few. Consequently, developing processing and learning tools for these graph data is more important than ever. Such a tools need not only capture the graph structure of the data but also account for the dynamics of the topology as practical graphs change over time.

In this PhD project, you will develop novel signal processing and machine learning theory for data over dynamic graphs leveraging insights from graph machine learning signal processing, graph neural networks, and graph representation learning. Such techniques will be applied to recommender systems and water networks.

The project will be carried out in the research group of Dr. E. Isufi, in collaboration with researchers at the faculty of EEMCS and Aidrolab. Dr. Isufi’s group at TU Delft works on fundamental research on graph signal processing and graph machine learning. We focus on both theoretical and applied research especially to recommender systems (in the Multimedia Computing Group) and water networks (in Aidrolab).

You will be offered quite a flexibility in the project, hence candidates able of working independently, eager to learn, and grow as scientific researchers are prefered. You will also be collaborating 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 any field related to the research topic;
• A strong mathematical background in statistical learning, linear algebra and optimization theory.
• A solid understanding in one of the following: machine learning, pattern recognition, statistical signal processing.
• A background in network science or graph-related data processing and learning, but not mandatory;
• 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 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 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 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 here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.

For information about the application procedure, please contact Dr. E. Isufi at [email protected]     

Are you interested in this vacancy? Please apply before 31 January 2023 via the application button and upload:

• A cover letter stating your motivation in this particular PhD project and why would you be a suitable candidate for the position (max 1 page);
• A detailed curriculum vitae (max 3 pages);
• A research statement stating your vision of what do you see as potential research directions for your PhD (max 3 pages);
• A list of all courses taken with grades both in the bachelor and master;
• Names and contact information of two academic referees;

For information about the application procedure, please contact Dr. E. Isufi at [email protected]     

A pre-employment screening can be part of the selection procedure.

You can apply online. We will not process applications sent by email and/or post.

Acquisition in response to this vacancy is not appreciated.

We are seeking for highly-skilled and self-motivated candidates with a strong mathematical background to do a Ph.D. on the fundamental aspects of signal processing and machine learning over dynamic graphs.

Graphs are playing an ever increasing role in nowadays systems as a flexible tool to model complex systems. In addition, these systems generate a vast amount of data which can be modelled as signals or features over these graphs. This is for instance the case of infrastructure networks such as water, energy and transportation networks but also the case of social, brain, and financial networks to name a few. Consequently, developing processing and learning tools for these graph data is more important than ever. Such a tools need not only capture the graph structure of the data but also account for the dynamics of the topology as practical graphs change over time.

In this PhD project, you will develop novel signal processing and machine learning theory for data over dynamic graphs leveraging insights from graph machine learning signal processing, graph neural networks, and graph representation learning. Such techniques will be applied to recommender systems and water networks.

The project will be carried out in the research group of Dr. E. Isufi, in collaboration with researchers at the faculty of EEMCS and Aidrolab. Dr. Isufi’s group at TU Delft works on fundamental research on graph signal processing and graph machine learning. We focus on both theoretical and applied research especially to recommender systems (in the Multimedia Computing Group) and water networks (in Aidrolab).

You will be offered quite a flexibility in the project, hence candidates able of working independently, eager to learn, and grow as scientific researchers are prefered. You will also be collaborating 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 any field related to the research topic;
• A strong mathematical background in statistical learning, linear algebra and optimization theory.
• A solid understanding in one of the following: machine learning, pattern recognition, statistical signal processing.
• A background in network science or graph-related data processing and learning, but not mandatory;
• 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 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 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 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 here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.

For information about the application procedure, please contact Dr. E. Isufi at [email protected]     

Are you interested in this vacancy? Please apply before 31 January 2023 via the application button and upload:

• A cover letter stating your motivation in this particular PhD project and why would you be a suitable candidate for the position (max 1 page);
• A detailed curriculum vitae (max 3 pages);
• A research statement stating your vision of what do you see as potential research directions for your PhD (max 3 pages);
• A list of all courses taken with grades both in the bachelor and master;
• Names and contact information of two academic referees;

For information about the application procedure, please contact Dr. E. Isufi at [email protected]     

A pre-employment screening can be part of the selection procedure.

You can apply online. We will not process applications sent by email and/or post.

Acquisition in response to this vacancy is not appreciated.