PhD Candidate: Graph Neural Networks for Electricity and Gas...
0.8 - 1.0 FTE
Gross monthly salary
€ 2,541 - € 3,247
Research University Degree
Faculty of Science
14 September 2022
Are you interested in machine learning and how it can be used to make energy systems more sustainable and reliable? Do you want to work on state-of-the-art algorithms and apply them to real-world problems? Then join us as a PhD candidate working on graph neural networks for electricity and gas networks.
As the number of energy sources constantly increases and the goal of pursuing sustainable energy is as important as ever, making predictions in this domain becomes more challenging. As a PhD candidate, you will work on developing and applying machine learning algorithms, in particular Graph Neural Networks (GNNs), to electricity and gas networks and related data generated within Alliander. By working on the state-of-the-art machine learning and AI methods in a multidisciplinary environment, you will help to make energy systems more sustainable and reliable.
Various applied problems within Alliander will be tackled using this class of methods, for example, resilience assessment of distribution grids and finding the best place for intelligent components, circuit breakers, and grid openings to minimise annual total outage. From a methodological point of view, the emphasis will be on uncertainty quantification for GNNs as well as domain adaptation for the generalisation of GNNs to unseen grids. You will be supervised by a multidisciplinary team with expertise in both computer science and energy grids consisting of Prof. Tom Heskes, Dr Yuliya Shapovalova, Tom van de Poll and Jacco Heres.
You hold an MSc degree in computer science, AI, data science, mathematics, electrical engineering or a related field.
You are proficient in programming languages used in scientific computing, such as Python, and have a strong willingness to develop programming skills further.
A strong background in statistics/machine learning and experience with deep learning are advantageous.
You are fluent in verbal and written English.
You have excellent communication, presentation and writing skills.
You are comfortable working in a multidisciplinary team.
The Data Science group is part of the Institute for Computing and Information Sciences (iCIS) at Radboud University. We develop theory and methods for machine learning and apply them in various fields. During recent evaluations, iCIS has been consistently ranked as the No. 1 Computing Science department in the Netherlands. Evaluation committees praise our flat and open organisational structure and our ability to attract external funding. Alliander is a one of the largest distribution system operators in the Netherlands, maintaining and operating the medium-low voltage grid and connecting end-users with generators to the transmission high-voltage grid. With the goal to enhance the transport capability of the distribution grid, our lab aims to address distinct challenges arising in the Netherlands and the thousands of distribution grids worldwide.
We want to get the best out of science, others and ourselves. Why? Because this is what the world around us desperately needs. Leading research and education make an indispensable contribution to a healthy, free world with equal opportunities for all. This is what unites the more than 24,000 students and 5,600 employees at Radboud University. And this requires even more talent, collaboration and lifelong learning. You have a part to play!
It concerns an employment for 0.8 (5 year contract) - 1.0 FTE (4 year contract).
The gross starting salary amounts to €2,541 per month based on a 38-hour working week, and will increase to €3,247 from the fourth year onwards (salary scale P).
You will receive 8% holiday allowance and 8.3% end-of-year bonus.
You will be appointed for an initial period of 18 months, after which your performance will be evaluated. If the evaluation is positive, the contract will be extended by 2.5 years (4 year contract) or 3.5 years (5 year contract).
You will be able to use our Dual Career and Family Care Services. Our Dual Career and Family Care Officer can assist you with family-related support, help your partner or spouse prepare for the local labour market, provide customized support in their search for employment and help your family settle in Nijmegen.
Working for us means getting extra days off. In case of full-time employment, you can choose between 29 or 41 days of annual leave instead of the legally allotted 20.
Additional employment conditions
Work and science require good employment practices. This is reflected in Radboud University's primary and secondary employment conditions. You can make arrangements for the best possible work-life balance with flexible working hours, various leave arrangements and working from home. You are also able to compose part of your employment conditions yourself, for example, exchange income for extra leave days and receive a reimbursement for your sports subscription. And of course, we offer a good pension plan. You are given plenty of room and responsibility to develop your talents and realise your ambitions. Therefore, we provide various training and development schemes.
Would you like more information?
For questions about the position, please contact Tom Heskes, Professor at email@example.com.
Practical information and applying
You can apply until 14 September 2022, exclusively using the button below. Kindly address your application to Tom Heskes. Please fill in the application form and attach the following documents:
A letter of Motivation (max. 2 pages).
Your CV (max. 2 pages).
A transcript of grades from all higher education degree programmes (BSc, MSc).
Your MSc thesis or (if not yet completed) another academic/technical writing sample.
The first round of Interviews will take place in the week of 10 October. You would preferably begin employment as soon as possible.
We can imagine you're curious about our application procedure. It offers a rough outline of what you can expect during the application process, how we handle your personal data and how we deal with internal and external candidates.