Sustainable Engineering Chair
PhD position (I) in engineering at Brussels Institute for Thermal-Fluid Systems and Clean Energy (BRITE),
a joint research project between the Université libre de Bruxelles (ULB) and Vrije Universiteit Brussel (VUB)
“Smart grid development combining machine learning and physical models: a grey-box modelling approach”
In the coming years, the electric grid will need to evolve drastically in order to meet the demands imposed by climate change and the clean energy revolution. The increasing presence of intermittent renewable energy is also affecting the reliability of the grid, making it harder to match supply and demand. The development of smart grids can be a solution to this problem. A smart grid can be conceptualized as a digital twin of the electric grid, capable of predicting its behavior in real time. Machine learning, together with the ever-growing availability of sensors, offers the tools to build such a digital model of the grid. Machine-learning techniques have already been employed with great success to discover fundamental patterns, forecast the behavior of dynamical systems and model the input-output relationship of complex systems.
The objective of this PhD project is to apply machine learning and deep learning techniques for the development of smart grids using a grey-box approach, combining physics-based models with novel data-driven methods, such as graph neural networks.
Offer Description
This is a PhD project at the Brussels Institute for Thermal-Fluid Systems and Clean Energy (BRITE), in the framework of the Sustainable World Initiative & Fellowship For Transformation (SWIFFT). SWIFFT is a joint research project funded by the Helios Foundation that brings together researchers from ULB and VUB to tackle different aspects of the energy transition, ranging from sustainable engineering, transformation and wellbeing to finance and law for a just energy transition (www.swifft.be). The PhD project will be co-supervised by Prof. Aurélie Bellemans and Dr. Alberto Procacci.
Prof. Aurélie Bellemans is working on the integration of novel data-driven concepts in the field of thermo-fluids (i.e., thermodynamics, fluid mechanics, heat transfer and combustion) to understand and optimize challenging engineering applications in aerospace and renewable energy. The overarching topic of her research is to develop data-driven feature-extraction methods and build advanced surrogates using machine-learning algorithms.
Dr. Alberto Procacci works on the development of reduced-order models, with a focus on physically constrained models, forecast of dynamic systems and data assimilation. His overall research goal is to combine physical knowledge with state-of-the-art machine-learning techniques to develop surrogate models that can be employed as digital twins.
Requirements
Skills and Qualifications
We seek a candidate with a strong background in the following fields:
1 – Machine-learning and deep learning models,
2 – Electrical engineering,
3 – Numerical modelling.
Offer requirements
- A Master of Science in engineering, data science, physics, or applied mathematics with a focus on either energy engineering, electrical engineering or machine-learning,
- A qualification equivalent to first-class honors degree is preferred,
- Experience in numerical methods, excellent computational skills, expertise in programming (Python, Fortran, C++),
- Interest in deep learning, data-driven modelling, optimization,
- English language is mandatory.
Selection process
The selection process is based on two steps:
- Evaluation of the documents provided by the applicant,
- Interview of each candidate having the eligibility requisites (evaluated through the first step). Interviews will be organized remotely.
The list of documents to be provided:
The list of documents to be provided:
- Letter of motivation (approx. 1 page),
- Copies of degree and academic transcripts (with grades and rankings),
- Summary of the master’s thesis (approx. 1 page),
- Short CV including a publication list (if any),
- Two reference letters from academics,
- Proof of English language skills.
Where to apply?
Please submit your application through the SWIFFT portal using the button below, or send it to:
Please name your documents following this format: SURNAME-NAME_NameOfTheDocument_position1.pdf. (ex.: SURNAME-Name_CV_position1.pdf; SURNAME-Name_MotivationLetter_position1.pdf)