Collaborative Mentoring Groups and Their Initiatives
Our interdisciplinary study groups form the core of the SustAInability collaboration — connecting students and mentors who design, build, and analyze real-world sustainability applications across Germany, Cambodia, and Kyrgyzstan.
Develops solar-powered LoRa sensor nodes and low-cost data loggers for temperature, humidity, and air-quality monitoring. The network supports comparative field studies across partner campuses and local communities.
View group →Applies machine-learning models to analyze environmental data, detect anomalies, and forecast short-term climate trends. The team integrates Python-based analytics and embedded AI on microcontrollers.
View group →Explores how quantum algorithms and simulators can contribute to sustainable optimization problems — from energy distribution to efficient logistics and material science education.
View group →Uses IoT sensor networks and AI analytics to monitor water quality, detect leaks, and forecast consumption in urban systems. The project combines environmental engineering with data-driven decision tools and is coordinated by Prof. Ulrike Gayh at SRH Heidelberg.
View group →Integrates climate topics into teaching and outreach. Students develop lesson materials, citizen-science kits, and public engagement tools to foster awareness and practical action.
View group →Building on the 2025 milestones, the teams are preparing follow-up proposals to expand the Global Sensor Network and AI modules toward new partner sites. Planned activities include joint field deployments, open-data dashboards, and hybrid training courses under future DAAD and EU frameworks.