PhD Scholarships in edge machine learning for high-performance power converters and drives - DTU Wind

Do you want to be involved in the green energy transition? Do you wish to contribute to the development of machine learning methods with real-world impacts on power electronic converters and drives? Are you looking for a career in R&D or academia? We have multiple opportunities for you!

We invite applications for multiple 3-year fully funded PhD positions that will contribute to the development of a new generation of embedded intelligence within grid-connected power converters and variable-frequency motor drives with edge computing and machine learning capabilities.

We offer a multidisciplinary, international, and friendly atmosphere, encouraging creativity, diversity, empathy, and teamwork. You will have a great opportunity to build strong networks with internationally known researchers at DTU and other universities as well as industrial partners.

You will be affiliated with the Power to X and Storage section in the Division of Power and Energy Systems, at the Department of Wind and Energy Systems. Collaboration is encouraged with neighboring sections working on distributed energy systems, hybrid power plants, smart grids, power system flexibility, and market design and operation. The section undertakes internationally leading research and teaching in the fields of flexibility, advanced power electronics, battery energy systems, and microgrids. We are currently 30 researchers from 15 different nationalities, and our different perspectives are foundational to our approach to solving real-world problems.

The PhD positions are part of the European Research Council (ERC) project ARTEFACT, which aims to make power converters and drives smarter, more reliable, and able to better support the power grid by embedding intelligence directly on the edge. The project will develop decentralized and privacy-preserving online learning methods to enable fleets of motor-interfacing converters (for applications such as pumps, compressors, or fans) to autonomously quantify and aggregate their flexibility, enabling their participation in grid-supporting services while accounting for their underlying uncertainty.

Depending on the specific position and your background, your work will involve developing machine learning methods for reliable training on streaming data, physics-informed models, Bayesian modeling, and stochastic optimization. These are complex topics and will involve many considerations and involvement with stakeholders, and it is important to state that you will not be alone. We truly believe that it is us and our joint contributions, with our different backgrounds, mindsets, competences, knowledge, and ideas that enables us to find the right solutions.

Responsibilities and qualifications

If you are, like us, motivated by seeing this project become reality, we are already on the right track. We also hope that you will have experience in the following areas:

• Knowledge of computer science and machine learning.
• Familiarity with electrical and electronic engineering.
• Proficiency with programming languages such as Python, C++, or MATLAB.
• Strong problem-solving skills and the ability to collaborate in interdisciplinary teams.
• Excellent command of the English language and communication skills, with the ability to present results in scientific papers.

You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree.

Approval and Enrolment

The scholarships for the PhD degree are subject to academic approval, and the candidates will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education.

Assessment

The assessment of the applicants will be conducted by Professor Tomislav Dragicevic and Postdoctoral Researchers Pere Izquierdo and Miguel Lopez.

We offer

DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and appointment terms

The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years.

The PhD project must start in 2026. The exact start date will be subject to a mutual agreement, considering your availability and preference, and will be earliest 1 July 2026. The position is full-time.

Application procedure

Your complete online application must be submitted no later than 15 June 2026 (23:59 Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

• A letter motivating the application (cover letter)
• Curriculum vitae
• Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale

Incomplete applications will not be considered. You may apply prior to obtaining your master's degree but cannot begin before having received it. Applications received after the deadline will not be considered.

All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates for the position.