• Artificial Intelligence
• Advanced Data Analytics
• Power Electronics
• Digital Twin
• Physics-Informed Machine Learning
• Predictive Maintenance and intelligentcontrol

• Fundamentals of power electronics
• Fundamentals of probabilistic models and statistical analysis
• Experience with MATLAB/Python
* Please get familiar with Python basics and set up your Google Colab account before the course. A tutorial of Google Colab can be found:https://www.tutorialspoint.com/google_colab/google_colab_tutorial.pdf
* Matlab installed with the predictive maintenance toolbox. You may find more details in the below link:
https://www.mathworks.com/products/predictive-maintenance.html

By the end of the course, students will be able to:
1. Explain the fundamental concepts of artificial intelligence and advanced data analytics relevant to power electronics.
2. Apply AI and data-driven methods to model, analyze, and control power electronic systems across design, operation, and maintenance stages.
3. Implement selected AI techniques in Python/Matlab for real-world case studies, including digital twin and physics-informed approaches.
4. Critically evaluate the performance, limitations, and applicability of AI-based solutions in power electronics applications.
5. Integrate theoretical knowledge with practical skills to produce a complete project report demonstrating problem-solving competence.

The course counts for 3 ECTS credit. All students will receive a diploma stating participation in the course.

Remarks - workload:
In total about 84 hours (18 hours teaching, 12 hours practicing, 24 hours preparation, 20 hours finalizing the student report, and 10 hours examination). Theteaching and practicing hours correspond to the three-day on-site lectures and hands-on sessions. Preparation includes pre-course reading of the assignedliterature, software setup, and review of prerequisite concepts. The finalizing of the student report covers data analysis, documentation, and refinement ofproject results. Examination refers to the preparation and submission of the final report, including potential revisions based on feedback.

Evaluation form: The course is accompanied by a hands-on team project so that the theoretical tools introduced in the course can be implemented in real applications. The
course evaluation will be based on the project report.

Criteria for assessment: Student performance will be assessed based on the following criteria: 1. Technical accuracy and depth – Correct application of AI and advanced data analytics
methods to the assigned power electronics problems, with clear justification of the chosen approach. 2. Practical implementation skills – Ability to implement the discussed methods in Python/Matlab, including correct use of tools, data processing, and model validation. 3. Critical analysis – Demonstration of understanding in evaluating results, discussing limitations, and suggesting improvements or alternative solutions. 4. Quality of the final report – Clarity, structure, completeness, and adherence to academic standards in the written report. 5. Engagement and collaboration – Active participation in discussions, teamwork in the project, and constructive contributions during the hands-on sessions. The final grade will be determined based on the project report (main component) and the student’s active participation throughout the course. 

Selected articles, book chapters, presentations and software will be announced to the participants at the course site in PhD moodle. 

Please register here at the website of The Doctoral School in Engineering and Science here.

Please observe that it is impossible to register for PhD courses until 4 months before start

Outside of AAU
If you are from outside Aalborg University and you wish to enroll in one of our PhD courses, you must create a profile. This is done by clicking on “create new account” and filling out the form in PhD Moodle.

Cancellation
Cancellations are accepted no later than 2 weeks before the start of the course. The Doctoral School has a no-show fee of DKK 3.000 for each course where the student does not show up.

Registered illness sent to the course organizer and the aauphd@aau.dk is of course an acceptable reason for not showing up on those days.

Diploma
All passed participants will receive a diploma as a PDF file.
The Doctoral School kindly ask for your patience, as it might take a while before you receive the final course diploma. This is due to the process of gathering evaluations.

Please address questions regarding the evaluation or issue of the diploma to the Doctoral School, via email at phdcourses@adm.aau.dk

More information: www.energy.aau.dk/research/phd   

6000 DKK for PhD students outside of Denmark and 8000 DKK for other participants (Private, Industry and Universities) VAT-FREE Education)

PhD Student from Danish universities: The Danish universities have entered into an agreement that allows PhD students at a Danish university (except Copenhagen Business School) the opportunity to free of charge take a subject-specific course at another Danish university. Read more here: https://phdcourses.dk/ 

Guests at AAU Energy: Enrolled Guest PhD Students at AAU Energy can attend free of charge on the course if  available seats.