Image created by Jana Schor, 2024.

Data Science for Chem2Bdiv

We develop a data-driven platform that integrates chemical exposure and biodiversity data, and we create sophisticated visual insights into the regions where chemical concentrations impact biodiversity. By analyzing these patterns across chemical types, environmental matrices, and species, the project will help reveal the critical role of chemical pollution in biodiversity loss, supporting evidence-based decision-making for sustainable chemical management.

Goal: Contribute to a future where chemical use and design are driven by a profound understanding of their ecological impacts, ensuring the preservation of biodiversity and fostering a more sustainable relationship between human activities and the natural environment.

In this project we are working with:

• Graphs and networks to integrate data from different domains and resources
• Data Science approaches for Storytelling with Data to provide meaningful visuals
  
• Graph databases to store this knowledge as accessible knowledge graph.
• Large language models in combination with Retrieval Augmented Generation to provide human-like access to the integrated knowledge
  

Currently, there are 2 Master's thesis projects in progress:

1. Seamless Integration of Chemical and Biodiversity Data for Insightful Visuals

2. Empowering AI Conversations with Chemical and Biodiversity Knowledge Graphs

Please apply via the "Apply now" button in these job adds, or reach out for more details.