PhD Position Machine Learning on Graphs

13 Mar 2024
Job Information

Organisation/Company

Delft University of Technology (TU Delft)

Research Field

Technology

Researcher Profile

First Stage Researcher (R1)

Country

Netherlands

Application Deadline

15 May 2024 - 21:59 (UTC)

Type of Contract

Temporary

Job Status

Not Applicable

Hours Per Week

40.0

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

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 wind farms, solar grids and IoTs. Consequently, developing and using machine learning tools to process 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, we are looking for a candidate to work on one of the following fundamental areas:

• Physic-informed graph neural networks: The goal here is to leverage physic information from the medium (such as PDE models) to develop deep learning solutions for spatiotemporal data on graphs.
• Machine learning on higher-order networks: We want here to use more than pairwise information to learn representations from network data. In particular, we seek to develop and analyze deep learning solutions for this setting.
• Adaptive spatiotemporal learning over graphs: In this area, we want to study machine learning approaches on dynamic graphs by taking into account the evolution of the topology and of the respective signals.
• Application to renewables power forecasting: theoutcome of the research in the previous three pillars can potentially be applied on renewable power (e.g. wind) forecasting.

Candidates with other interest within the graph machine learning topic are also encouraged to apply by stating so in their application package. In whatever area, the candidate is expected to spend around 20% of their time on applying these techniques on renewables,

The project will be carried out in the research group of Dr. E. Isufi and co-supervied by Dr. H. Jamali-Rad from TU Delft / Shell. Dr. Isufi’s group at TU Delft works on fundamental research on graph signal processing and machine learning. We focus on both theoretical and applied research especially to recommender systems (in the Multimedia Computing Group) and water networks (in Aidrolab). Dr. Jamali-Rad’s group at TU Delft focuses on deep representation learning and self-supervised learning applied to variety of downstream tasks, including but not limited to computer vision. At Shell, Dr. Jamali-Rad leads a major portfolio of AI projects mostly focused on renewable power and biotechnology.

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 most affiliated. You will also be collaborating with other senior PhD researchers in the group and will supervise master and bachelor theses.

Requirements
Specific Requirements

We are looking for candidates with the following criteria:

• A Master’s degree in Computer Science, Data Science, Artificial Intelligence, Electrical Engineering, Applied Mathematics, or any field related to the research topic;
• A strong mathematical background in optimisation, statistical learning, linear algebra and probability;
• A solid understanding of machine and 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 Graduate Schools Admission Requirements .

Additional Information
Benefits

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.

Selection process

Are you interested in this vacancy? Please apply no later than May 15, 2024, via the application button and upload:

• A cover letter stating explicitly the area of interest and the motivation behind it as well as how you position yourself w.r.t. the above requirements (max 1 page);
• A research statement stating your vision of what do you see as potential research directions for your PhD (max 3 pages);
• A detailed curriculum vitae (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.

The deadline is 15 May 2024. However, we encourage you to submit your application earlier as we may fill the position as soon as a suitable candidate is found.”

Please note:

• 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.
• Please do not contact us for unsolicited services.

Additional comments

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

Website for additional job details

https://www.academictransfer.com/338964/

Work Location(s)

Number of offers available

1

Company/Institute

Delft University of Technology

Country

Netherlands

City

Delft

Postal Code

2628 CD

Street

Mekelweg 2

Geofield

Where to apply

Website

https://www.academictransfer.com/en/338964/phd-position-machine-learning-on-gra…

Contact

City

Delft

Website

http://www.tudelft.nl/

Street

Mekelweg 2

Postal Code

2628 CD

STATUS: EXPIRED