School of EngineeringOpen OpportunitiesThis 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
| 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
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