Current step-counting algorithms in wearable devices are inaccurate for older adults because they were designed and tested on younger populations. This creates measurement errors that undermine physical activity monitoring for the group that needs it most. This project will analyze accelerometer data from older populations to identify gait characteristics such as cadence, walking speed variability, […]

This project aims to develop an automated and robust satellite-based pipeline to map hedgerows, which are import for carbon storage, biodiversity and agricultural landscapes. Even though a large amount of satellite data is available, current methods often rely on manual labeling and are sensitive to changes in region or sensors. During this internship, a machine […]

This project aims to enhance the detection of modern cyberattacks by investigating quantum machine learning (QML) and quantum neural network (QNN) approaches alongside classical machine learning and deep learning models. As cyber threats evolve, attackers increasingly employ stealthy strategies, such as low-and-slow attacks and phishing/spear-phishing, designed to evade traditional detection systems. Moreover, many existing models […]

Online hate is increasingly spread through memes, where harmful meaning comes from the combination of images and text, making it harder to detect than text-only abuse. Through a 12-week research visit to the University of Hildesheim, I will develop and evaluate efficient AI models that can better identify hateful memes across multiple datasets while remaining […]

This project focuses on improving the trustworthiness of a RISC-V processor design intended for use in aircraft systems by carefully checking that it works correctly and securely. Modern aircraft depend heavily on software and digital hardware, so even small design errors or hidden security weaknesses can create safety risks. The project will verify a security-enhanced […]

The project advances the development of an AI-based system for automatic cutting tool condition characterization using machine learning and machine vision. It addresses the limitations of indirect tool wear monitoring by enabling direct, image-based wear analysis both on- and off-machine, with a focus on reducing downtime and improving tool utilization. A key challenge is adapting […]

This project explores two critical areas of AI research: cybersecurity for Internet of Things (IoT) systems and responsible AI assistants for education and research. IoT devices, widely used in healthcare, smart cities, and industry, are highly vulnerable to cyberattacks due to limited resources and large-scale connectivity. We propose AI-based intrusion detection systems that are lightweight, […]

This project aims to build an AI system that can turn regular, human-written math problems and solutions into fully checked, error-free formal proofs, with a special focus on quantum mathematics. The project will collect open-source quantum math problems, train a machine-learning model to translate them into formal proofs using the Lean proof assistant, and create […]

This project seeks to improve the understanding of how Machine-Learning (ML)-specific code issues arise during software development by using Large Language Models to analyze developers’ code changes over time. An automated framework will be created to track code updates, detect ML-related issues, and identify the type of developer activity (feature development, bug fixing, enhancement, or […]

La création d’images de synthèse ultra-réalistes nécessite des calculs de lumière complexes, souvent trop longs pour les besoins actuels. Les algorithmes récents (tels que ReSTIR) accélèrent le rendu en réutilisant les informations lumineuses d’une image à l’autre, mais ils risquent d’introduire des distorsions visuelles. L’objectif de ce stage est d’analyser les statistiques de ces méthodes […]

This project will help improve BenchBoss, a sports performance tracking app created by Breton Group, by moving it from an early prototype to a fully tested and reliable platform. The intern will research better ways to capture real-time performance data, build and validate easy-to-understand analytics dashboards, and test the app with youth coaches and parents […]

The objective of the project is to plan data collection for the design of machine learning models and algorithms capable of characterizing quantum interference between optical and quantum channels, or between quantum channels. We plan to use the NUMANA testbed to deploy the experiments to collect the required data. This will involve generating a quantum […]