Strategic PhD scholarship in AI-Based Dynamic Blackout Anticipation Methods for Future Power Systems – DTU Wind

Are you passionate about revolutionizing power systems with AI and digital twin technology? We seek a highly motivated PhD student to join our dynamic team at DTU. This is your opportunity to work on groundbreaking research to enhance future power systems' resilience and efficiency. By joining us, you will gain hands-on experience with state-of-the-art tools and methodologies, collaborate with leading experts in the field, and contribute to innovative solutions that address real-world challenges.

We look for a talented, self-motivated, and team-oriented individual who thrives in a collaborative environment and enjoys tackling complex topics. As a PhD student in our team, you will be part of a world-leading research environment and contribute to the development of next-generation AI-based tools for power systems. Your work will focus on developing dynamic blackout anticipation and prevention methods using a digital twin of future power systems. This involves developing AI-based dynamic security assessment tools to enhance decision-making and control processes, collaborating on employing a digital twin of an actual power system, the Bornholm Energy Island, and innovating solutions to improve power system resilience.

By joining us, you will not only advance your professional skills but also play a crucial role in pioneering research that supports a sustainable transition in the power sector. This project is funded by DTU Strategic PhD scholarship, through collaboration between the Division for Power and Energy Systems at the Wind and Energy Systems Department and the Section for Dynamical Systems at the Department of Applied Mathematics and Computer Science. As such, you will enjoy the benefits of having contact with two leading departments.

We are committed to fostering a diverse and inclusive environment and strongly encourage applications from individuals of all genders and backgrounds.

Responsibilities
You will be part of a vibrant working environment with a pleasant team atmosphere where you will have the opportunity to grow and develop your academic, industry, and personal skills. Besides cutting-edge research, PhD students participate in international research projects, collaborate with industry, assist in teaching courses, and co-supervise BSc and MSc students, allowing them to develop the necessary skills for the next steps in their career.

Qualifications
We are looking for candidates with diverse backgrounds to join our team. Graduates with a background in engineering, mathematics, computer science, computer engineering, physics, sustainable energy, and any related discipline are encouraged to apply. 

You are expected to conduct high-quality research on the intersection of machine learning, data science, power system simulation, and sequential decision-making, both from a theoretical/analytical point of view and coding. 

We seek self-motivated, team-oriented persons who thrive collaboratively and enjoy working with complex topics. 

A successful candidate will have a solid background in three or more of the following points: 

• Machine learning techniques, statistics, and probabilities
• Methodologies for decision-making under uncertainty (e.g. Stochastic/Robust Optimization, Dynamic Programming, Reinforcement Learning)
• Power system dynamics and stability
• Power system operation
• Programming tools such as Python, Julia, Pytorch, etc.
• Power system simulation software such as Powerfactory and PSCAD 

Furthermore, a successful candidate has

• Excellent use of the English language 
• Willingness to work in a team-oriented environment
• Good ability to present results in oral presentations and prepare scientific papers for publication in international journals

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 candidates will be based on the material they submit and one or two stages of interview, online and/or in person. During one of the two stages, the candidates will be asked to review a paper sent to them, describing its strengths, weaknesses, and potential for future extensions. 

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