Faculty of Business, Economics and InformaticsOpen OpportunitiesControllable Text Generation (CTG) refers to the ability to guide or influence the output of a generative language model to meet specific requirements or constraints. When integrated with Automatic Text Simplification (ATS), these constraints typically include compression ratio (the length of the simplified text divided by the length of the original text), lexical complexity, semantic similarity, and syntactic richness. By utilizing parallel complex-simple text pairs, ATS systems can be trained to generate simplifications that adhere to these specified requirements. - Text Processing
- Master Thesis
| Automatic Text Simplification (ATS) is a process that transforms linguistically complex text into a simpler version while preserving its original meaning. This transformation is crucial for making textual content accessible to specific populations, such as individuals with cognitive impairments. The core of ATS involves using a transformation function π, which maps an original text π to its simplified version, πβ². This function aims to maximize a user-specific utility function, predominantly focusing on enhancing information accessibility. - Image Processing, Text Processing
- Master Thesis
| In this project, we are going to develop a vision-based reinforcement learning policy for drone navigation in dynamic environments. The policy should adapt to two potentially conflicting navigation objectives: maximizing the visibility of a visual object as a perceptual constraint and obstacle avoidance to ensure safe flight. - Engineering and Technology
- Master Thesis
| Recent research has demonstrated significant success in integrating foundational models with robotic systems. In this project, we aim to investigate how these foundational models can enhance the vision-based navigation of UAVs. The drone will utilize learned semantic relationships from extensive world-scale data to actively explore and navigate through unfamiliar environments. While previous research primarily focused on ground-based robots, our project seeks to explore the potential of integrating foundational models with aerial robots to enhance agility and flexibility. - Engineering and Technology
- Master Thesis, Semester Project
| In this project, you will investigate the use of event-based cameras for vision-based landing on celestial bodies such as Mars or the Moon. - Engineering and Technology
- Master Thesis, Semester Project
| Our aim is to create an autonomous racing system capable of swiftly learning optimal racing strategies and navigating tracks more effectively (faster) than traditional methods and human drivers using RL. - Intelligent Robotics
- Master Thesis, Semester Project
| Use Inverse Reinforcement Learning (IRL) to learn reward functions from previous expert drone demonstrations. - Engineering and Technology, Intelligent Robotics
- Master Thesis, Semester Project
| Explore the use of large vision language models to control a drone. - Engineering and Technology, Intelligent Robotics
- Master Thesis, Semester Project
| Learn complex drone maneuvers from human feedback using Reinforcement Learning (RL). - Engineering and Technology, Intelligent Robotics
- Master Thesis, Semester Project
| This project focuses on developing robust reinforcement learning controllers for agile drone navigation using adaptive curricula. Commonly, these controllers are trained with a static, pre-defined curriculum. The goal is to develop a dynamic, adaptive curriculum that evolves online based on the agents' performance to increase the robustness of the controllers. - Engineering and Technology
- Master Thesis, Semester Project
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