At the division of Applied Mechanics and Energy Conversion (TME) of the Department of Mechanical Engineering, the Thermal Systems Simulation (The SySi) Team aims to sustainably use resources through integration and optimisation of thermal systems performance in the built and industrial environment, including other energy vectors. System integration is key.

Their scientific expertise can mainly be found in: (1) modelling and simulation: from detailed emulator and controller models to reduced models, using object oriented modelling, (2) optimisation and control: optimal design and optimal control, towards integrated optimal control and design (co-design); (3) experimental tests: from lab-scale to pilot plant and field tests to verify and validate models and methods as a proof-of-concept, also including real-life demonstrations. Starting from domain knowledge is key.

Research topics (most of them investigated in the frame of a PhD thesis) include among others: physics-based modelling (white-box and grey-box) toward white-box models enriched by data (zebra-box), white-box Model Predictive Control (MPC), Building Optimization Testing Framework (BOPTEST), from building level to clusters of buildings (collective concepts) to multi-sectorial integration, thermal networks (THERNET) for heating and cooling, flexibility through Demand Response (DR) in multi energy vector systems, including uncertainties towards robust design and robust control.

The SySi Team, led by prof. Lieve Helsen, has gained significant expertise and international recognition in the field of system integration for performance optimisation of thermal systems. White-box MPC in tertiary buildings is now being commercialised through the spin-off BUILTWINS. Today The SySi group consists of 1 professor, 2 post-doctoral researchers and 5 PhD students. The group will soon be extended with 1 post-doctoral researcher and 1 innovation manager. The research has very close links and is integrated within EnergyVille, a research collaboration between KU Leuven, VITO, imec and UHasselt on sustainable energy and intelligent energy systems.

This post-doc position is framed in and funded by the VLAIO-Moonshot-InduFlexControl II project, which is a collaboration between KU Leuven, VITO and UGent. The main research question is: How can we unlock and enable increasing amounts of flexibility in the energy-intensive industry by developing ground-breaking control techniques which will allow the incorporation of new sources of flexibility while remaining suitable for the overall eco-system?

In general terms, flexibility refers to the ability of a system to deviate from a given plan and respond to short term changes. However, in the energy-intensive industry, most of the processes are foreseen to run at almost full capacity to achieve the maximum profit. Thus, although flexibility is present in these systems its use in an energy context is not always obvious, as on most occasions that flexibility was conceived for a different purpose (e.g., reliability, processes interrelationship, etc.). 

To transform our energy system and industry into a sustainable, low carbon and climate-friendly eco-system we need to look into new alternatives. One enabler is the incorporation of increasing capacities of renewable energy sources (RES) into the grid or within industrial facilities. This comes at the cost of higher uncertainty in the energy supply due to the RES’ dependency on environmental factors. A solution to this is unlocking a higher level of flexibility at the consumer side, a flexibility that will be necessary to support the stable and secure operation, as well as guaranteeing system adequacy and resilience, while benefiting from low carbon emissions. 

With this fundamental idea, the preceding sprint-cSBO project investigated the exploitation of underlaying flexibility that could be found in the energy-intensive processes. The results have demonstrated the existence of large amounts of inherent flexibility in main processes (such as furnaces or electrolysers), as well as auxiliary system (e.g., cooling towers, compression systems, etc.). Nevertheless, when considering the expected profit, reliability of the system and production quality, further flexibility is needed to accommodate increasing levels of low emission energy sources. Within the range of available flexibility options, power-to-X solutions provide the highest flexibility potential, because of their large energy volumes (hundreds of MWh to tens of GWh), high power ratings (tens to hundreds of MW) and long-term storage potential (weeks, months, seasons). 

To unlock this flexibility potential, we aim to combine the unique potential of model-based and data-driven modelling and control approaches to provide a feasible solution. To guarantee the valorisation of the fundamental research question, these aspects are not considered in isolation, but strategic contextual factors are incorporated in the research plan: (i) the energy market design, and (ii) the power/energy network configuration, with their relevant constraints. 

The post-doctoral researcher in The SySi Team will focus on learning-based MPC to design a robust controller that handles uncertainties and disturbances. The main task is to design and develop robust, stable, lightweight, and self-tuning Deep Learning (DL)-based MPC which will handle constraints on input, output, and states, plant-model mismatch, disturbances on inputs and outputs, and guarantee closed-loop stability with less computational complexity. The potential of this novel approach needs to be (virtually) tested (incl. benchmarking with respect to conventional control strategies) and demonstrated for multi-carrier energy systems in industry and different types of storage, incl. power-to-X. 


We are looking for a highly motivated, enthusiastic, dynamic, mobile and communicative (senior) researcher with a PhD in Engineering. The candidate should have a strong background and interest in energy, and especially in energy systems (for energy-intensive industry), control (MPC enriched by learning techniques), (physics-based) modelling and optimisation. The candidate starts from insights in the system and physics-based modelling and enriches these approaches by learning techniques based on data. 
The candidate is expected to:
  • Be scientifically rigorous, initiative taking, results orientation, loyal
  • Be proficient in English (spoken and written) to allow effective communication, knowledge of Dutch is an asset
  • Be able to work independently, accurately and methodically
  • Be a team player, using the broad network offered
  • Be an active player in the broader team, contributing to coaching the junior researchers, attending brainstorm sessions, sharing approaches, insights and vision
  • Present research findings at national and international conferences
  • Publish research findings in international top-journals
  • Contribute to the InduFlexControl project as Scientific WP Leader for the DL-MPC part, but also for the communication, dissemination and valorisation tasks
  • Attend project meetings and write project reports 
This post-doc researcher will be fully funded through a VLAIO Moonshot project. The research is to be performed within The SySi Team in close collaboration with the InduFlexControl II project consortium members (KU Leuven The SySi Team and ELECTA Team, VITO, UGent), and in the framework of EnergyVille ( The location for this position will be Leuven (Department of Mechanical Engineering) and Genk (EnergyVille).
The successful candidates will receive:
  • An extensive international network of universities, companies and associations to work with
  • Multiple benefits (health insurance, access to university infrastructure and sports facilities, etc.) 
  • A very competitive salary, including social security
  • The opportunity to participate in research collaborations and international conferences
  • Both the city of Leuven, just 20 km east of Brussels, the heart of Europe, and the city of Genk, offer a stimulating, young and multicultural working environment