Master Thesis
vor 3 Monaten
Topic: Empowering AI Conversations with Chemical and Biodiversity Knowledge Graphs
Contract limitations
limited contract
- Contact
**Your contact for any questions you may have about the job**:
Prof. Dr. Jana Schor
Bio-Data Science Group
The UFZ
The Helmholtz Centre for Environmental Research (UFZ) with its 1,100 employees has gained an excellent reputation as an international competence centre for environmental sciences. We are part of the largest scientific organisation in Germany, the Helmholtz association. Our mission: Our research seeks to find a balance between social development and the long-term protection of our natural resources.
The job
Research on chemicals in our environment has revealed their significant role in shaping biodiversity patterns, yet integrating this data with biodiversity information remains a challenge. This project aims to bridge that gap by developing an automated data integration workflow that combines chemical exposure data with biodiversity records. The focus is purely computational, leveraging advanced large language model techniques, like retrieval augmented generation, to streamline the process of human-knowledge interaction. The project will enable researchers and stakeholders to explore the relationships between chemicals and biodiversity more effectively via a user-friendly, human-like interface to increase understanding of the connection between chemical and biodiversity research.
The overall vision of this project is to 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.
The position to prepare the Master's thesis will be supervised at the site in Leipzig.
Your tasks
In this Master's thesis, a ChatBot Python package will be developed. Large language models (LLMs) will be used and grounded with a knowledge graph of integrated biodiversity and chemical data. Retrieval augmented Generation (RAG) will mitigate knowledge gaps, factuality issues, and hallucinations of (ungrounded) LLMs with external/domain-specific knowledge. The ChatBot will serve as a user-friendly, human-like interface for non-computer scientists, stakeholders, or the public for the collected data.
The tasks include:
- Craft a knowledge graph from an integrated data collection on biodiversity and chemical monitoring data, and use a graph database to store this data
- Develop a ChatBot Python package (utilizing an existing in-house prototype) based on large language models
- Develop a retrieval augmented generation solution to ground this LLM with knowledge stored in a Graph Database
- Engineer prompts to allow correct responses to the project-relevant questions
- Provide showcases
We offer
- Excellent supervision that supports your personal and professional development
- Exciting insights into the work of a leading research institute
- The chance to work in interdisciplinary, international teams and benefit from a wide range of perspectives
- The opportunity to contribute and actively shape your own ideas and impulses
right from the start
- Modern technical equipment and IT service to optimally support your work
**Profile**:
- Background in Computer Science
- Advanced understanding of large language model (LLMs) APIs
- Solid programming skills in Python
- Experience with software development in an IDE (JetBrains PyCharm)
- Experience with collaborative software development and agile project management with Git
- Database experience and database querying languages, preferably with graph databases and CYPHER
- Fluent in spoken and written English
Application deadline: 15.11.2024
Diversity and Inclusion
The UFZ has a strong commitment to **diversity** and actively supports **equal opportunities** for all employees regardless of their origin, religion, ideology, disability, age or sexual identity.
Important
Contact
**Your contact for any questions you may have about the job**:
Prof. Dr. Jana Schor
Bio-Data Science Group
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