PhD in Real-time stream data analysis for offshore wind turbine monitoring

The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for a PhD stipend in the field of Real-time stream data analysis within the general study programme Electrical and Electronic Engineering; as per August 1st, 2026, or as soon as possible thereafter.

In electronic engineering, Aalborg University is known worldwide for its high academic quality and societal impact. The Department of Electronic Systems employs more than 200 people, of which about 90 are PhD students, and about 40 % of all employees are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and results into applications in close collaboration with industrial partners worldwide. You can read more about the department at www.es.aau.dk.

Your work tasks

Modern wind turbine testing and verification face a series of data-intensive challenges that require coordinated advances from both data-science and electronics research. Wind turbines, especially prototype units—generate massive volumes of heterogeneous, high-frequency telemetry, including vibration and acoustic data sampled at tens of kilohertz, yet they are typically deployed in remote onshore or offshore locations with narrow, failure-prone communication channels.

The PhD student will work reliable, resilient, and performant ML-based solutions to efficiently transmit and analyze high-frequency (tens of KHz), high-velocity multimodal data streams, thereby enabling critical-event monitoring of offshore wind turbines.

The challenges faced in these situations stem from the need to detect extremely low-probability turbine failure modes (10?³–10?5), where even small data-loss events undermine statistical confidence. The aim is to develop i) edge intelligence (on-turbine smart algorithms for data preprocessing), ii) resilient data movement (error-tolerant, cybersecure, standards-compliant communication layers while mitigating corruption and synchronizing high-rate sensor streams), and iii) inference generalization from single prototypes (extrapolate from single-unit test turbine measurements to expected variability across large real-world turbine populations).

Your competencies

We are looking for a candidate who is driven by complex, interdisciplinary challenges and has a strong foundation in either data science, electronics, or systems engineering—with a deep curiosity to learn the others.

Professional qualifications:

• Educational background: You hold an MSc in Computer Science, Electrical/Electronics Engineering, Data Science, Cyber-Physical Systems, or a closely related discipline.
• Machine Learning & Data Processing: You have solid experience developing and applying machine learning algorithms, ideally with a focus on time-series data, anomaly detection, or edge computing (TinyML).
• IoT: You have a strong understanding of IoT communication protocols, data resilience, or cybersecurity principles. Familiarity with signal processing, particularly with high-frequency sensor data (such as acoustics or vibration) is an advantage.
• Programming skills: You are proficient in Python for data science and ML prototyping. Experience with C/C++ or embedded programming for deploying algorithms on edge devices is highly desirable.
• Statistical intuition: You understand the challenges of working with sparse data and low-probability events, and you are interested in how to extrapolate findings from a single system to larger populations.

Personal and social qualities:

• Collaborative mindset: This project sits at the intersection of data science, electronics, and wind energy. You are comfortable working alongside experts from different fields, asking questions when you step outside your core domain, and sharing your own expertise generously.
• Navigating ambiguity: Researching rare failure modes in unpredictable environments means things will not always go according to plan. You approach roadblocks systematically and maintain momentum even when troubleshooting complex, heterogeneous data issues.
• Communication: You can translate complex, highly technical concepts into clear language, whether you are writing an academic paper, discussing requirements with hardware engineers, or presenting at a conference.
• Self-driven: While you will be fully supported by your supervisors and the research group, you take ownership of your project, proactively steering its direction and proposing new methodologies.

Qualification requirements

PhD stipends are allocated to individuals who hold a Master's degree. PhD stipends are normally for a period of 3 years. It is a prerequisite for allocation of the stipend that the candidate will be enrolled as a PhD student at the Technical Doctoral School of IT and Design in accordance with the regulations of Ministerial Order No. 1039 of August 27, 2013 on the PhD Programme at the Universities and Certain Higher Artistic Educational Institutions. According to the Ministerial Order, the progress of the PhD student shall be assessed at regular points in time.

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)
• 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.