EPFL - Ecole Polytechnique Fédérale de Lausanne

Open Opportunities

Scaling Synthetic Data Generation for Foundation Models in Prognostics and Health Management EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section We are offering a paid internship opportunity at the EPFL IMOS lab to explore innovative data generation techniques that enhance the capabilities of Foundation Models. In this role, you will investigate synthetic data creation for Prognostics and Health management (PHM) scenarios, working towards pretraining a foundation model for PHM and scaling synthetic data generation to millions of datasets. You’ll gain hands-on experience with cutting-edge Machine Learning tools, collaborate with researchers, and help shape the future of data-driven PHM. If you're eager to take on the challenge of scaling data generation for Foundation Models, we’d love to hear from you! Data Storage Representations, Data Structures, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Simulation and Modelling, Software Engineering Internship, 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

Automating Reward Function Design Using LLMs for Robotic Disassembly of EV Batteries EPFL - Ecole Polytechnique Fédérale de Lausanne Automatic Control Lab This project explores the integration of large language models (LLMs) into a reinforcement learning (RL) pipeline to automate reward function design for the robotic disassembly of end-of-life electric vehicle (EV) batteries. By iteratively refining reward functions using LLMs, the approach aims to enhance training efficiency, accelerate learning, and improve the precision and safety of complex disassembly tasks. Utilizing Nvidia Isaac Sim for simulation and transferring skills to real-world robots, the research seeks to reduce human intervention in reward engineering, providing scalable solutions for advanced robotic manipulation in battery recycling and beyond. Intelligent Robotics, Mechanical Engineering, Systems Theory and Control, Text Processing, Virtual Reality and Related Simulation Master Thesis

Frictional Instabilities in Topologically Interlocked Structures EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section Topologically interlocked structures (TIS) represent a new class of innovative designs inspired by the mechanics of puzzles. Constructed from individual building blocks that interlock without the use of adhesives, these structures exhibit remarkable mechanical properties, relying solely on contact and frictional forces for their integrity. Experimental observations have revealed sudden failures and sharp load drops in TIS, indicating that frictional slip instabilities play a significant role in their structural response. This project aims to explore the influence of stick-slip frictional instabilities and interfacial heterogeneity on the failure mechanisms of TIS. Using the level-set discrete element modeling framework, the student will investigate the dynamic behavior of these systems under various conditions. The project offers an opportunity to delve into the unique mechanical behavior of TIS and gain experience with modern computational tools in structural engineering. Civil Engineering, Materials Engineering Master Thesis, Semester Project

Multiphysics Modeling of Fault Gouge EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section The fault gouge, a layer of cohesionless material formed by fragmentation of parent rock, plays a key role in the macroscopic frictional behavior of faults, including their stability and energy release. This material exhibits complex behavior influenced by mechanical deformation, thermal effects and pore fluid flow. In this project, we utilize a combination of discrete and continuum simulations to investigate gouge rheology. In particular, the student will explore the effect of material heterogeneity and grain-scale characteristics on the macroscopic behavior, including the influence of particle fracture. Additionally, phenomena arising from hydromechanical and thermomechanical coupling will be studied. The findings from the project aim to provide new insight into earthquake mechanics. Civil Engineering, Earth Sciences, Materials Engineering, Physics Master Thesis

Physics-informed Deep Neural Operators for Solid Mechanics EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section Recently, physics-informed neural operators (PINOs) have been introduced as a new approach for solving complex problems in engineering, by combining data with knowledge of the underlying governing equations. The concept is an extension of previously successful purely data-driven deep neural operators. In this project, the student will explore the application of PINOs on solid mechanics problems, with the goal of simulating the behavior of materials under various loading conditions. Applications will be considered in the context of geotechnical or structural engineering. The generalization capabilities of the method will be evaluated, and its accuracy will be compared to conventional numerical solutions. The findings aim to advance computational tools for engineering design and analysis, bridging the gap between traditional numerical methods and scientific machine learning. Aerospace Engineering, Civil Engineering, Mechanical and Industrial Engineering Master Thesis, Semester Project

Segregation in Granular Flows EPFL - Ecole Polytechnique Fédérale de Lausanne ENAC - Civil Engineering Section Segregation in granular flows is an important phenomenon influencing various natural and industrial processes, from landslides to pharmaceutical manufacturing. This project investigates the role of particle shape in segregation dynamics within granular flows. Using numerical simulations, the student will analyze how particles of varying shapes and sizes segregate under shear and gravity-driven conditions, by analyzing the forces developed between particles. The final goal is to provide insights into the continuum modeling of segregation phenomena in different scenarios. Civil Engineering, Earth Sciences, Physics Master Thesis, Semester Project

Nanoengineered surfaces for boiling heat transfer enhancement EPFL - Ecole Polytechnique Fédérale de Lausanne Institute of Mechanical Engineering Context: boiling is a phase-change phenomenon with broad applications, especially important for thermal management and power generation. Scientific challenge: simultaneously improving boiling efficiency and maximum heat flux Approach: understanding boiling enhancement mechanisms and manipulating bubble behaviors with well-defined surface nanostructures fabricated using clean room technologies Knowledge required: background in heat transfer, fluid mechanics, thermodynamics, and mechanical design. Hands-on experience with experiments/FEM modeling software will be a plus. Skills that you will acquire: design of boiling setup; high-speed imaging; use and design of resistive temperature detectors; scientific communication through reports and presentation Fluidization and Fluid Mechanics, Heat and Mass Transfer Operations, Mechanical and Industrial Engineering, Nanotechnology Bachelor Thesis, Master Thesis, Semester Project