PhD scholarship in Smart Grids: Embedded Machine Learning to Unlock Demand Flexibility - DTU Wind

Jobbeskrivelse

Are you passionate about the green energy transition? Do you wish to contribute to the development of solutions to support the increasingly renewable power grid? This fully funded 3-year PhD position at DTU Wind and Energy Systems could be a perfect fit for you.

We offer a vibrant, multidisciplinary, international, and friendly environment, 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 leading industrial partners.

As a PhD candidate, you will join the Power-to-X and Storage (PtX) section within the Power and Energy Systems (PES) division. The PtX section combines multidisciplinary research in energy storage and flexibility, digital twins, energy islands, electrolyzers, and machine learning. Our team of 25 members from 13 different nationalities values diversity and includes experts in a broad range of scientific disciplines, including electrical engineering, control theory, industrial engineering, electronics engineering, energy policy, data science, and applied mathematics.

As part of the Alliance program, your project will be co-supervised by Associate Professor Qianwen Xu at KTH, with the opportunity to undertake a research stay of 3-6 months in Stockholm, Sweden.

Responsibilities and qualifications

Power electronic converters are increasingly deployed not only to integrate renewables, but also to control potentially flexible loads such as pumps, fans, EV chargers, and others. With their large energy consumption, converter-interfaced assets hold significant potential to act as decentralized, virtually cost-free sources of flexibility that can stabilize renewable-heavy grids—provided they are equipped with the right intelligence.

This PhD project will leverage the power of field-programmable gate arrays (FPGA) to deploy machine learning models on the edge with low latency and high energy efficiency. This added intelligence will autonomously estimate the flexibility of converter-interfaced loads, communicate with an aggregator, and modify their decentralized control to contribute to the resilience and reliability of increasingly renewable energy systems.

The ideal candidate will have experience or an interest in the following areas:

• Knowledge of computer science and machine learning.
• Familiarity with electrical and electronic engineering.
• Proficiency in 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.

We welcome candidates with varied experiences and educational pathways. DTU Wind works with an inclusive mindset in recruitment. If you find the position interesting but do not think you meet all our requirements, apply anyway—you could be the ideal candidate for us.

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 scholarship for the PhD degree is subject to academic approval, and the candidate 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 January 2026. The position is full-time.

Further information

Further information may be obtained from Professor Tomislav Dragicevic (tomdr@dtu.dk).

You can read more about DTU Wind and Energy Systems at https://wind.dtu.dk/

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU.

Application procedure

Your complete online application must be submitted no later than 31 October 2025 (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

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.