EPFL - Ecole Polytechnique Fédérale de Lausanne

Open Opportunities

Fusing Physics-based and Machine Learning Models for Building Energy Modeling EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section This project is a collaboration between the EPFL Intelligent Maintenance and Operations Lab and the EMPA Urban Energy Systems Lab exploring building thermal modeling that combines data-driven and physical modeling to optimize building energy system operation. Engineering and Technology Bachelor Thesis, Master Thesis, Semester Project

Physics-Inspired Graph Neural Networks for Modeling of Building Thermal Dynamics EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section This project is a collaboration between the EPFL Intelligent Maintenance and Operations Lab and the EMPA Urban Energy Systems Lab exploring Building Thermal Modeling by fusing Graph Neural Network models with Reduced Order Models to optimize building energy system operation. Engineering and Technology Master Thesis, Semester Project

Master Thesis - Graph Neural Networks for Granular Mechanics EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section Granular materials form complex networks of force chains arising from frictional interactions between particles. Under applied shear, this network of contacts can undergo complex topological and geometrical rearrangements. The connection between these grain-scale patterns and the macroscopic behavior of the material is still a field of active research. In this project we will employ Graph Neural Networks (GNNs) to shed light on these processes, focusing on the regime where granular materials approach unjamming and failure. The models will be trained on data from high fidelity discrete element simulations as well as experimental measurements with grain-scale resolution. Geotechnical Engineering, Mechanical Engineering Master Thesis, Semester Project

Physics-informed neural ordinary differential equations for degradation modeling EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section In many real-world complex systems, the output is influenced by both the input, such as operating conditions, and the system's health status. To accurately predict the dynamics of such systems, it's essential to not only understand the relationship between input and output based on various health conditions, but also to model the system's degradation over time. In this project, our objective is to combine physical knowledge to develop a neural ordinary differential equation (ODE) to model this degradation, thereby enabling precise predictions of the system's dynamics over time. Here, we will mainly focus on two systems, the Lithium-ion battery and CMAPSS jet engine. Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project

Optimizing HVAC Commissioning: A Deep Learning Approach for Parameter Prediction and Error Reduction EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section The project is a collaboration between EPFL IMOS Lab and Belimo exploring Deep Learning algorithms for commissioning parameters of Heating, Ventilation, and Air Conditioning systems Engineering and Technology Master Thesis

PhD student to investigate the effect of air composition on the respiratory virus transmission chain (AirTRAC) EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Environmental Science and Engineering Section Since the COVID pandemic it is well-acknowledged that airborne respiratory viruses significantly contribute to disease transmission. Nevertheless, our current understanding of the physicochemical processes that affect the infectivity of respiratory viruses in the aerosol and the susceptibility of the next host to airborne infection are limited. In this multi-collaborator project we will investigate the influence of air composition on the transmissibility of airborne viruses. Environmental Sciences, Microbiology PhD Placement

Multimodal Out-of-Distribution Detection with Foundation Models EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section The student will work on the topic of Multimodal Out-of-Distribution (OOD) Detection. The goal of this project is to explore the rich information in Foundation Models to improve the OOD Detection performances. Artificial Intelligence and Signal and Image Processing Master Thesis, Semester Project

Visual condition monitoring of Railway retaining walls with machine learning EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section This project is part of a collaboration between the IMOS lab and Matterhorn Gotthard Bahn, a railway company operating in the Swiss Alps. The student will work on developing computer vision algorithms for automated visual inspection of retaining walls around railway tracks. Retaining (or supporting) walls are crucial infrastructure elements responsible for maintaining the structural integrity of terrains around railway tracks and ensure safe operation. They are subject to wear and damages including cracks, concrete cancer (i.e., alkali–silica reaction), displacements, erosion and water infiltration. Images of retaining walls have already been collected and labels are available. The goal will be to design algorithms to estimate the condition of a wall, with a focus on robustness, transfer learning, and explainability (XAI). Engineering and Technology, Information, Computing and Communication Sciences Master Thesis, Semester Project