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  3. PhD System-theoretic Agents for End-to-End Learning for Energy System

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PhD System-theoretic Agents for End-to-End Learning for Energy System

Challenge: Transforming the conventional carbon-intensive energy use
Change: AI to turn data into knowledge for efficient systems
Impact: Boosting the sustainable, fair and reliable energy transition

3 maanden geleden


Mekelweg, Delft, Zuid-Holland
Tijdelijk contract / Tijdelijke opdracht
Uren per week:
38 - 40 uur
€ 2395 - € 3061 per maand


TU Delft is a top tier university and is exceedingly active in the field of Artificial intelligence and the TU Delft's campus has 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. The DAI Energy Lab 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. The DAI Energy Lab investigates data-driven scientific modelling for their applicability in monitoring the 'health' of energy system components, and for the early detection of threats. We are currently a team of four PhD researchers and 2 co-directors Dr. Jochen Cremer and Dr. Peyman Mohajerin Esfahani. You will extend the team and integrate your own ambitious research program within our research vision. We distinguish between IN-AI and WITH-AI research. IN-AI projects focus on fundamental methods from data-driven scientific computing for energy system applications. WITH-AI projects focus on assembling such methods to build full workflows for the application to energy system problems.

This WITH-AI PhD project focuses on system-theoretic agents for end-to-end learning for energy systems. Along with your colleagues, you will work on real-time control for power systems by investigating end-to-end and AI-based controllers, both for centralised and decentralized control agents. Your task is to develop machine learning methods that can encode implicitly knowledge from the physical system within novel training workflows as "physics-layer", and regularise those model with those knowledge. You will use techniques from differentiable programming together to develop new neural network architectures suitable for power system applications, and combine them with mathematical optimization and reinforcement learning. You will develop those methods in close collaboration with researchers who are experts in control systems for power systems, and experts in machine learning.

The research in the Department of Electrical Sustainable Energy is inspired by the technical, scientific and societal challenges originating from the transition towards a more sustainable society and focuses on three areas:

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

The Electrical Sustainable Energy Department provides expertise in each of these areas throughout the entire energy system chain. The department owns a large ESP laboratory assembling High Voltage testing, DC Grids testing environment and large RTDS that is actively used for real time simulation of future electrical power systems, AC and DC protection and wide area monitoring and protection.

The Intelligent Electrical Power Grid (IEPG) group, headed by Professor Peter Palensky, works on the future of our power system. The goal is to generate, transmit and use electrical energy in a highly reliable, efficient, stable, clean, affordable, and safe way. IEPG integrates new power technologies and smart controls, which interact with other systems and allow for more distributed and variable generation.


  • An MSc degree in either Machine Learning, Robotics, Control Systems, Operations Research or in Power/Energy Systems, Electrical Engineering, etc.
  • Demonstrated competences in one or more of these categories: AI, computer/data science, machine learning, energy system modelling, dynamic systems, power systems or another relevant field.
  • An affinity with teaching and guiding students
  • A proven record and interest in further developing your modelling, programming, analytical and scientific writing skills
  • An affinity with energy and power systems, with net-zero carbon targets, technical challenges
  • Proficient in verbal and written English.
  • The ability to work in a team, take initiative, be results oriented and systematic


TU Delft offers PhD-candidates a 4-year contract, with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2395 per month in the first year to € 3061 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.

Additional information

For information about this vacancy and the selection procedure, please contact Jochen Cremer, Assistant Professor, email: j.l.cremer@tudelft.nl.