TU Delft

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Originele vacaturetekst

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

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

Job description

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
Conditions of employment

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.

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 and aim to be as inclusive as possible (see our Code of Conduct). Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale.

Challenge. Change. Impact!

Faculty Electrical Engineering, Mathematics and Computer Science

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three disciplines - electrical engineering, mathematics and computer science. Combined, they reinforce each other and are the driving force behind the technology we use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make future-proof. We are also working on a world in which humans and computers reinforce each other. We are mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. There is plenty of room here for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1,100 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.

Additional information

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

Application procedure

Are you interested in this vacancy? Please apply before August 31, 2021 via the application button and submit the following in three pdf files: (pdf 1) 1-page Motivation letter, your CV; (pdf 2) a (part of your) M.Sc. thesis or a paper that you have written, in which you demonstrate your writing skills, (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.
  • Acquisition in response to this vacancy is not appreciated.
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