AI Lab

AI Lab supports the Faculty of Natural Sciences’ AI‑related needs in two main areas:

Large Language Models & Chatbots

Supporting researchers and educators in adopting LLM‑powered technologies for teaching and research, and providing access to faculty-wide chatbot solutions.

Click HERE for guides and templates for prompting, the chatbot interface, and more.

For questions or collaboration inquiries, please contact: ai-lab@au.dk or anneka@phys.au.dk

AI Development & Engineering

Collaborating closely with researchers across departments, including designing, developing, and validating advanced analytical and generative AI solutions for large research datasets, as well as developing prototypes.

For questions or collaboration inquiries, please contact: ai-lab@au.dk or anan@clin.au.dk or anan@phys.au.dk

For examples of our AI engineer's work, please visit: https://github.com/Anne-Andresen or https://anne-andresen.github.io/

Scope of LLMs & Chatbot Branch

What we do:

Help integrate LLM‑based technologies into courses, research workflows, and academic projects.
Provide access to chatbot interface systems based on local infrastructure and open-source.
Help facilitate custom chatbots for research groups or courses.
Advice on best practices for prompting, workflows, LLM-technology implementation, and tool selection.

Who's it for:

Researchers in the Natural Sciences seeking support in applying LLM‑based tools to their projects and/or domains, independent of their existing familiarity with AI.
Educators in Natural Sciences exploring how AI can be incorporated into their teaching or course design and who require appropriate infrastructure.

Scope of AI Development & Engineering Branch

What we do:

Consult with researchers to assess their project needs and develop suitable deep‑learning approaches that align with their research context and data.
Collaborate on research‑driven prototypes and technical proofs‑of‑concept, depending on project scope and feasibility.
Provide guidance on model training, architecture, evaluation, and deployment considerations.
Offer access to local computing infrastructure upon request.
Assist with assessing computational needs, including GPU usage, resource planning, and scaling considerations.

Who's it for:

Researchers in the Natural Sciences who require guidance on choosing or applying AI approaches, independent of their existing familiarity with AI.
Researchers in Natural Sciences working with large datasets, imaging data, pattern recognition, complex modelling tasks, or similar.

Revideret 15.04.2026

Cecilie Højlund Simonsen