PhD Position Machine Learning on Graphs
3 weken geleden
We are seeking for a highly-skilled and self-motivated candidate with a strong mathematical background to do a Ph.D. on the fundamental aspects of graph machine learning with applications to renewables.
Job 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:
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: the outcome 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.
We are looking for candidates with the following criteria:
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.
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.
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.
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 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.
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