Master's thesis: AI and Software for Intelligent Buildings in the Energy System

We have up to three open positions for dedicated master’s students who want to join us at the Computer Science department at RISE.

Background and Purpose
We are developing next-generation AI and software technologies to make our buildings and energy systems more intelligent, energy-efficient, and resilient. The digitalisation of buildings is particularly challenging, due to the differences between monitoring and automation systems between each building. Data normalisation and uniform structuring of information, together with new data-driven modeling approaches, can drastically reduce the time to deploy digital solutions in buildings. This is key to achieve our sustainability and energy targets, considering that buildings account for a large portion of energy used globally. They will also make buildings more comfortable to be in.

Your thesis will be based on two ongoing projects, TwinVista and ThermFlex, that are collaborations between researchers at RISE and KTH together with a large number of industry partners. AI and digital twinning technologies are starting to play an interesting role in the smart and sustainable transitions of building and energy systems, and there is strong interest from industry in these topics. You are expected to collaborate with researchers from both RISE and KTH, to develop solutions based on AI and data-driven modeling for building energy management and building digital twinning.

Thesis Description
We can offer the following topics for your master thesis as different options based on your interests, and there is some flexibility to define the specifics based on your profile and personal development goals:

• Develop AI Agents for Building Digital Twin Applications.
• Using AI and Computer Vision to create knowledge graphs of building data and information assets.
• Data Engineering for Simulation Environments in Building Digital Twins.
• Data-driven model for building sensor systems redundancy elimination.
• Develop tools to work with and integrate large semi-structured building data/information assets with each other.
• Data mapping and analytics using Physics-informed machine learning with numerical modeling results, such as Computational Fluid Dynamics (CFD).

Your main activities will depend on the specific topic we agree on, but will involve coding (mostly in python), and/or development of AI models, and/or mathematical modeling.

Terms:

• Start time: as soon as possible.
• Scope: 30hp (20 weeks)
• Location: RISE Computer Science, Stockholm (Kista) or KTH Byggvetenskap. Options to partially work remotely.

Who are you?
You have an interest or are curious about building and energy applications, you enjoy challenging yourself to learn new things, and thrive when collaborating with other researchers in an international environment.

We expect you to already have good programming skills (in python) and experience in applying machine learning models. Depending on the specific topic, we additionally expect you to either have familiarity with Deep Learning and Large Language Models, or with mathematical modeling, data engineering, and control theory.

Welcome with your application!
To know more, please contact Dr. Thomas Ohlson Timoudas  (thomas.ohlson.timoudas@ri.se) or Dr. Joakim Eriksson (joakim.eriksson@ri.se). Applications should include a brief personal letter, CV, recent grades, and a code example. Candidates are encouraged to send in their application as soon as possible but at the latest by the 15th of January 2024. Suitable applicants will be interviewed as soon as applications are received.