Join AI:Wind-Lab: Two PhD stipends in bridging control, applied mathematics and AI for fault diagnostics of modern wind turbines

Join AI:Wind-Lab: Two PhD stipends in bridging control, applied mathematics and AI for fault diagnostics of modern wind turbines.

Structural Health Monitoring aims at monitoring the health of in-service structural systems for the purpose of their lifetime assessment, a topic of particular interest to expand the lifetime of wind turbines. Despite remarkable progress in structural health monitoring boosted by AI, purely data-driven models have no physical interpretability and poor generalization capabilities. Thus, they are ill-suited for inference about system's health in rapidly changing environment of wind turbines. Although physical laws can be enforced to learn a model whose parameters can be physically interpreted, there may be groups of different parameters producing the same output as the true system parameters, making it almost impossible to uniquely recover the actual ones.

AI:Wind-Lab aims to develop a set of fundamental AI methods together with their software implementations for interpretable statistical fault prediction and lifetime assessment in the context of Structural Health Monitoring of operating wind turbines.

Two PhD stipends are available for an appointment.

About AI:X Labs

AI:X is an ambitious initiative at Aalborg University that aims to advance AI research and create real-world impact through interdisciplinary collaboration, and nine labs will be initiated in 2026 with a total of 18 PhD stipends.

The AI:Wind-Lab is part of the AI:X initiative with two PhD stipends and is a collaboration between the Department of Electronic Systems, the Technical Faculty of IT & Design and the Department of Mathematical Sciences, the Faculty of Engineering and Science.

The stipends are open for appointment from August 15, 2026, or soon hereafter. The duration of the position is three years.

About the applicants

Stipend 1: The first PhD candidate should be mathematically oriented, with a strong background in control, and systems theory, skills related to modelling of mechanical systems and interest in fault diagnosis. This PhD candidate will focus on statistical lifetime assessment of a wind turbine system by combining parametric reduced order models of operating wind turbines, information contained in their differential geometry and safe data-driven control/reinforcement learning tools jointly developed with the second PhD candidate.

Stipend 2: The second PhD candidate should have a strong background in mathematics combined with interest in modern AI methods and differential geometry. The candidate will develop a safe data-driven control /reinforcement learning approach to explore the space of parameters in the context of optimising fault detection. An important part of the PhD will be devoted on controlling the probability of detections, for example using conformal prediction or other uncertainty quantification methods.

Both PhD candidates are expected to collaborate closely with each other and with the researchers in AI:Wind-Lab.

Your competencies

Prospective applicants should have the following qualifications:

• M.Sc. degree in either automatic control, mathematical engineering, mechanical engineering, mathematics, computer science or similar.
• Solid mathematical and analytical skills.
• Experience in programming in Python or another language, e.g., in C++, Matlab, R.
• Familiarity with basic concepts of dynamical systems.
• Knowledge of wind turbine dynamics is a plus.
• Curiosity to research and develop new solutions in collaboration with senior and junior researchers within AI:Wind-Lab.
• Readiness to collaborate with another PhD student and the researchers from other disciplines within AI:Wind-Lab.

How to apply

Your application must include the following:

• Application, stating reasons for applying and qualifications in relation to the position
• Curriculum Vitae (CV)
• Diplomas (bachelor's and master's degree diploma, including grades)
• Max. 1 page summary of the topic of your MSc thesis and optionally up to 1 page self-written text on how your knowledge can be used in the project.
• Other relevant documents

The application must be submitted via Aalborg University’s recruitment system, which can be accessed under the job advertisement on Aalborg University's website.

Aalborg University wants to reflect the surrounding society and has diversity as a core value. Therefore, everyone, regardless of personal background and orientation, is encouraged to apply for the position.