Bachelor and Master Theses

** Work extensible to 2 students **

AI can play a pivotal role in enhancing digital twin technologies, especially when developing a digital twin version of an industrial objective.

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 by adopting and customising specific learning techniques, then implement and evaluate their model.

If two students apply, the model should also be integrated within an interactive animation context.

The project will run in cooperation with the ABB Corporate Research Centre, 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 of testing aspects, or cloud computing, or networking, or Digital Twin technologies is a plus

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

4. Familiarity with tools such as Matlab and Simulink.

5. General good skills in programming (if the animation topic is applicable).

6. Proficiency in English, both spoken and written.

Technical Skills

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