Thesis – Factual Ingestion Using Sustainability Boundary Conditions

This thesis proposes an efficient AI/ML database ingestion function, combining research papers in different areas of sustainability with sustainability boundary condition checks, to create factual examples of sustainability problems across sectors such as energy, agriculture, transport, etc. The core research topic is how to model and implement the sustainability boundary condition check based on Swedish research – how to produce valid examples from strict, human language-based criteria?

About the company

Sustainable AI Solutions, SAIS, is a new, ground-breaking Swedish AI and sustainability start-up developing sustainability solutions based on AI technologies. Their goal is to accelerate the transformation of sustainability with digitalization and AI. SAIS is still in the beginning stages. Join them now and contribute to the exciting journey.

The role

We are seeking two thesis students with a passion for AI/ML and sustainability. This is a unique opportunity to experience the start-up phase alongside professional sustainability experts. This role will collaborate with other thesis workers and company employees.

Your profile

• You have a solid background in data, computer science, system design, or an IT, programming, AI/ML education path.
• You have experience in using modern AI tools and know of both risks and opportunities.
• You are a good team player and able to interact with friendly users and various researchers.
• You are eager to learn more, innovative, and scientific, with an interest in sustainability.
• You have an ethical and respectful approach to data and science.

Thesis details

There is a need to scale up fact-based sustainability actions from organizations, companies, and governments. This thesis is to select and trial different AI/ML components and technologies to ingest sustainability research, evaluate its proposals towards a set of sustainability boundary conditions, and create proposed examples of non-sustainability, which will be used to drive sustainability discussions in organizations and companies wanting to change in a related Master’s Thesis work. The database would have a maintenance interface and a semi-automatic quality control, based on self-assessed accuracy. This accuracy will then be evaluated by a human-in-the-loop, leading to accuracy improvement iterations. The technologies involved will partly be up to the student to propose, evaluate and compare from efficiency and accuracy of the result. Previous work exist.