This PhD position is offered in collaboration with the Intelligent Maintenance and Operations Systems (IMOS) Laboratory at EPFL, and Urban Energy Systems Laboratory (UESL) at EMPA.
Application link: https://apply.refline.ch/673276/2121/pub/1/index.html
Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real-world energy applications, the project aims to better capture the dynamics of urban infrastructures across different spatial and temporal scales, from building-level energy demand to district-scale interactions and their integration with wider energy networks.
Your profile
You are a highly motivated and talented candidate with a Master’s degree in Engineering, Control, Computer Science, Physics, Applied Mathematics, or a related field. You bring a strong analytical background and are proficient in areas like geometric deep learning, signal processing, statistics, or learning theory. Knowledge of energy systems, multi-energy infrastructures, or urban energy applications is a strong asset. You are self-driven, creative, and bring strong problem-solving skills as well as the ability to work in an interdisciplinary environment. Proficiency in English (spoken and written) is required; good comprehension and oral skills in German are desirable.
The IMOS Lab at EPFL is offering a paid, on-site summer internship in collaboration with an industrial partner. The project focuses on a decantation problem and aims to develop a computer vision and machine learning solution based on both simulated and real data.
The internship will involve working with an existing C++ simulation pipeline with CUDA to generate synthetic data, and using this data together with real observations to train and evaluate machine learning models. The work sits at the intersection of simulation, computer vision, and applied machine learning, with direct relevance to an industrial use case.
We are looking for a student with:
- Good programming skills in C++
- Familiarity with CUDA or GPU programming
- Experience in machine learning and computer vision
- Interest in working with both simulated and real-world data
- Ability to work independently in a research environment
Practical information
- Location: On site at EPFL
- Duration: Summer internship
- Compensation: Paid
Contact
Ismail Nejjar
[email protected]