Bachelor and Master Theses

AI can play a pivotal role in enhancing digital twin technologies.

The combination of AI and digital twins offers dynamic, real-time simulation, optimization, and predictive capabilities, allowing, among others, a reduction of costs from energy bills.

The student will develop a prediction model on an actual smart home system with a multitude of sensors and actuators.

The student will adopt and custome specific learning techniques, then implement and evaluate their model under developed scenarios.

The project runs within the Vinnova project D-RODS (A Digital Twin Framework for Dynamic and Robust Distributed Systems).

Academic Background

1. A solid understanding of digital systems engineering principles.

2. Basic knowledge of machine learning. Knowledge (Arduino) microcontrollers, cloud computing, or Digital Twin technologies is a plus.

3. Prior coursework on embedded systems, machine learning / AI.

4. Familiarity with tools such as Matlab and Simulink.

5. General good skills in programming.

6. Proficiency in English, both spoken and written.

Technical Skills

1. Experience with programming languages such as Python may come as a plus.