This position is offered through the ANU Computing Internship ([COMP4820] / [COMP8830])
Company
Research Graph Foundation researchgraph.org
The Research Graph Foundation is a not-for-profit and collaborative venture to connect scholarly records across global research repositories. The Foundation contributes to developing capabilities that transform disconnected and siloed research activities into a connected network of scholarly works.
Project
Knowledge base about AI Technologies
This internship aims to build a knowledge base about applied AI technologies including the use of large language models (LLM), computer vision, and other AI models. This internship consists of knowledge translation projects where we read academic and technical articles, and write blog posts that make these technologies easily understandable and accessible to the general public. In addition, the interns will present these technologies in seminars and publish LinkedIn posts that showcase the latest AI technologies. The interns will create knowledge graphs as open datasets about scholarly articles and related topics. These graphs will be animated on https://researchgraph.org. Finally, the interns will work with the Research Graph team to write Python code that demonstrates AI-assisted functions, test AI platforms and implement AI pipelines.
There are five components defined as separate projects in this internship:
Project 1: Knowledge base about AI-Assisted Software Development: This project focuses on tools and platforms that support software engineering and programming. Examples of such platforms include Microsoft Copilot and Zed.dev. Experience with using AI to write code would be advantageous for this project.
Project 2: Knowledge base about AI in Health: This project centres on the application of AI technology in medical science. It involves collaboration with researchers from medical science backgrounds, aiming to create a graph of connected publications related to digital health, with a focus on AI technologies. One of the outcomes of this project will be an academic article, with the aim of publishing in a medical science journal. Experience with publishing academic articles will be an advantage for this internship.
Project 3: Knowledge base about LLM new models: This project involves testing, understanding, and producing working code examples, tutorials, and blog posts about Large Language Models and other text generation models available on https://huggingface.co. Experience with running LLM models on personal computers with a GPU is essential for this internship.
Project 4: Knowledge base about AI Pipelines: This project focuses on creating AI pipelines using emerging tools such as https://dify.ai and https://comfyuiweb.com. Experience with running LLM models on personal computers with a GPU is essential for this internship.
Project 5: Knowledge base about Image Generation Models: This project centres on open-source models such as Stable Diffusion and FLUX.1, along with related prompt engineering techniques and platforms. Experience with image generation models will be an advantage for this project.
The outcome of these projects is aligned with the Research Graph Foundation’s commitment to Open Science, and all produced materials and code will be publicly accessible under an open-source licence.
Required/Preferred Technical Skills
- Essential: Experience in using Python for data analytics, and prior experience of working with large language models and prompt engineering.
- Bonus: Linux environment experience.
Required/Preferred Professional/Other Skills
Ability to work independently and take initiative while knowing when to ask for help and communicate with others. Having curiosity and diligence are needed for a research project as the ability to collaborate with a small team.
Delivery Mode
Remote.
Type of internship
Unpaid Placement.
How to apply
Applications are invited from students who have already passed the eligibility checks for the Computing Internship courses COMP4820 or COMP8830. Further information about the Computing Internships can be found on the Computing Internship page.
You can nominate multiple preferred Internship projects and host organisations through the one application form.