The attached document presents the offers of Master’s Final Project topics for the 2025-26 academic year by professors and research groups of the Department of Electronic Engineering.

The titles of the offered topics are listed below.

• Prototyping and evaluation of spiking neural networks on neuromorphic platforms
• Development of reinforcement learning algorithm systems for drone guidance
• Development of reinforcement learning algorithm systems for playing chess
• Brain-computer interfaces
• Modeling human behavior by processing signals obtained from motion sensors
• Metamaterials for invisibility
• Training a neural network applied to satellite imagery
• Design and characterization of surface acoustic wave resonators in the GHz range
• Development and validation of chiplet interconnection
• Modeling persuasion strategies in ads using multimodal language models (MM-LLMs)
• Predicting multimedia content recall using brain signals
• Design and implementation of emotional recognition techniques through body motion modeling
• Design and implementation of sign language recognition techniques using multimodal motion modeling
• Applying MLLMs to predict biometric responses in audiovisual neuromarketing
• Implementation of an agent-based chatbot for brand and corporate identity management
• Implementation of a distributed physical activity recognition system with inertial sensors
• Design and implementation of teaching content for Artificial Intelligence courses
• Fast Vocabulary Adaptation for ASR
• Multicultural Personalization for Social Robots
• Multimodal Attention Understanding
• Artistic Painting Animations – Bringing Art to Life with AI
• Remembering Art – Can VLLMs Recreate Masterpieces?
• Design and implementation of a software interrupt controller for RISC-V
• Design and development of a high-bandwidth FPGA test system
• Design and development of a test bench for embedded systems with RTOS
• Modelling of 2D Charge-Density-Wave Materials probed with Surface Acoustic Wave Devices
• Study and implementation of a pipeline for structural connectomics in premature infants at risk of autism