Postdoc Applied Probability Theory for Machine Learning Tasks in Complex Energy Systems

Collaborate&Learn: TU Delft and AIT

Challenge: Renewable energy transition

Change: Turn data into knowledge for efficient systems

Impact: Boost the sustainable, reliable energy transition

TU Delft is a top tier university and is exceedingly active in the field of Artificial Intelligence (AI) with a strong expertise in energy systems. Energy systems are the backbone of our modern society, but are becoming increasingly complex and challenging to operate as renewable energy, heating and transport sectors are integrated into the system. It’s crucially important that energy systems are sustainable, reliable and effective, now and in the future. TU Delft’s research investigates how the new area of data-driven and scientific computing can contribute to managing energy systems.

We combine ground-breaking machine learning with the reliable theory of the physical energy system. The area of data-driven scientific computing promises to combine statistics, time-frequency analysis, low-dimensional model reductions, and other techniques to extract information from data. With machine learning, we make such information useful for the management of complex energy systems. For example, it is possible to use neural networks to model differential equations that describe dynamics, and for predicting extreme, rare events. Together, the Inteligent Eletrical Power Grids research section and the Delft AI Energy Lab has currently a team of 20+ researchers investigating data-driven scientific modelling for their applicability to complex energy systems. There, you will work closely with Associate Prof Dr. Simon Tindemans and Assistant Prof Dr. Jochen Cremer. You will extend the team and integrate your own ambitious research program within our research visions.

This Postdoc research project is on the theme of sampling operating conditions and network configurations of time-varying systems, closely along a PhD researcher. Along with your colleagues, you will apply probability theory to the transmission and distribution electicity systems. Your work is foundational and we expect a high impact as many machine learning tasks require synthetic generated data. However, currently, the models to generate data have errors due to shifts in data or modelling inaccuracies. Society and grid operators require urgently novel methods to sample various grid topologies and operating conditions, so the energy transition can be accelerated. These sampled conditions require to be physically feasible, relevant and important for the downstream machine learning task. Your methods will be generalizable to many machine learning tasks, resolving an issue typically hindering the adoption of machine learning workflows - the “lack of data”.

This research is part of a multi-partner, large-scale international collaboration with TenneT, EPRI, INESC TEC, DTU and others. You will closely work with our international partners to maximise success of your Postdoc project. There, your task is to design a data synthesizer that realises the methods you develop. You will integrate this synthesizer into a platform so other can test, experiment and verify the next level of AI-application for the Delft Control Room of the Future (CRoF).

About the department

The research in ESE Department is inspired by the technical, scientific, and societal challenges originating from the transition towards a more sustainable society and focuses on four areas:

• DC Systems, Energy Conversion and Storage (DCE&S)
• Photovoltaic Materials and Devices (PVMD)
• Intelligent Electrical Power Grids (IEPG)
• High Voltage Technologies (HVT)

• A PhD degree in Applied Probability, Signal Processing, Power Systems, Energy Markets, Energy Systems, or Computer Science, or Applied Machine Learning to any of the aforementioned.
• Highly technical and academic competences, experience with academic publishing and guiding students.
• Organisational skills.
• Must have demonstrated competencies in one or more of these categories: AI, computer/data science, machine learning, energy system modelling, power systems, and energy markets.
• An affinity with teaching and guiding PhD and MSc students.
• A proven record and interest in further developing your modelling, programming, analytical and scientific writing skills.
• The ability to work in a team, take the initiative, be results-oriented and systematic

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.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 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 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.

For information about this vacancy and the selection procedure, please contact Jochen Cremer, Assistant Professor, email: [email protected];  For questions regarding the recruitment process please contact Brenda Reyes, Management Assistant at [email protected] .     

The applications for this post should be submitted through the application button in the recruitment system. A motivation letter can be addressed to Dr. J. Cremer.

Submit the following in three pdf files: (pdf 1) 1-page Motivation letter and your CV; (pdf 2) a scientific paper that you have written, (pdf 3) your MSc and BSc transcripts of grades and courses taken.

Please highlight in your motivation letter and/or CV examples of projects and achievements that demonstrate your relevant competences.      

• 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.