Postdoc in Data Science for Reliability, Structural Integrity and Lifetime Assessment of Wind Energy Assets - DTU Wind

Would you like to join a team that brings solutions towards the green energy transition, by developing data-driven tools and methods that ensure the structural health and reliable and efficient operation of wind energy assets? The Structural Integrity and Load assessment (SIL) section at DTU Wind and Energy Systems is inviting candidates to apply for a Post doc in Data Science for Reliability, Structural Integrity and Lifetime Assessment of Wind Energy Assets, considering both onshore, bottom-fixed, and floating offshore wind turbines.   

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. We develop talent by offering a career mentor, state-of-the-art research infrastructure, and postgraduate teacher training. 

Responsibilities and qualifications
You will be part of the SIL team that works on: 

• Risk, Reliability Engineering and RAM (reliability, availability, maintainability) analysis​, 
• Probabilistic design, assessment and uncertainty quantification, 
• Data, Digitalisation, AI/ML and decision support systems (I.e cyber-physical systems)​, 
• Materials and monitoring, 
• Service life integrity assessment and end of life scenarios​,
• Project valuation and Life Cycle Cost modelling and
• Wind farm-wide operational strategies​.

As a Reliability, Structural Integrity and Lifetime Assessment specialist, you are expected to be fluent in data science and analytics, and have a solid knowledge in asset integrity assessment and management strategies for structural and mechanical components. You shall assume a leading role in the research activities leading to improved lifetime and asset integrity assessment, and ultimately achieve cost reduction for wind turbine structures and main components. 

This requires the development of numerical and data-driven models, integration and analysis of monitoring data, development of KPIs (key performance indicators), and integrated decision support systems towards deployment of autonomous cyber-physical systems. This is done in collaboration with the SIL section and the department, in national, EU and industry sponsored projects. Your primary responsibilities will be:

• Collect, process and analyse datasets from operational wind farms, extracting maximum value through data fusion and analytics. 
• Develop multidisciplinary models (decision support frameworks) capable to integrate different criteria in the development of operation and maintenance strategies, including reliability-centred maintenance, risk-based inspection and other state of the art methods.
• Develop/contribute to the development of digital twin models for life prediction and operational optimisation of selected turbine structures and components through physics-based or data driven models.
• Application of advanced statistical and computational techniques, to effectively quantify and analyze uncertainties within wind turbine components and operations and the models supporting them. Your work will ensure robust and reliable assessments under varying conditions.
• Crafting sophisticated machine learning models, including deep learning, sequence models, reinforcement learning and generative models, specifically tailored to address challenges in wind energy systems. Your models will enable more accurate predictions and decision-making.
• Publish your research findings in prestigious international journals and present your work at renowned international conferences.
• Contribute to the formulation of grant applications for national and European calls to secure funding for cutting-edge research.
• Actively participate in the academic community by teaching and supervising BSc and MSc students at DTU. You will also have the opportunity to support the supervision of PhD students within the department.

You may get the opportunity to contribute to the teaching of courses. 

The following qualifications are relevant to this post:

Essential qualifications:

• Educational Background: A background in mechanical, civil, or wind turbine engineering with a strong focus on applied mathematics, statistics or data analytics applied to wind energy systems.
• Experience with data analytics and machine learning applications, particularly in areas related to structural integrity, load assessments, and reliability analysis of wind turbine components and systems.
• Capability of numerical model development and use in complement with Machine Learning and AI methodologies is essential for maximizing the performance of decision support systems.
• Experience with scientific programming (Python, MATLAB, or C).

Desired qualifications: 

• Experience with risk-based methods and structural reliability
• Advanced Machine Learning Expertise: Demonstrated advanced expertise in cutting-edge machine learning techniques, such as deep learning, reinforcement learning, sequence models and generative models, as applied to wind energy systems and particularly anomaly detection topics. 
• Surrogate Modeling: Experience in utilizing ML-based surrogate modeling (regression) techniques to efficiently approximate complex simulations and data in the context of reliability analysis, structural integrity assessment, uncertainty quantification and decision support for wind energy.
• Research Proposal Development: Demonstrated expertise in formulating and crafting persuasive research proposals tailored to machine learning and AI applications. Proven track record in securing research funding and driving successful grant applications.

As a formal qualification, you must hold a PhD degree (or equivalent). 

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 terms of employment
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 2 years. Starting date is 01 June or ASAP then after.

You can read more about career paths at DTU here.

Further information  
Further information may be obtained from Professor Athanasios Kolios, Head of Section, atko@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.

Application procedure 
Your complete online application must be submitted no later than 28 April 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:

• Application (cover letter)
• CV
• Academic Diplomas (MSc/PhD – in English)
• List of publications 

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.

DTU Wind and Energy Systems is one of the largest and most well-known university department for wind energy in the world with 400 employees. The institute is in the international driving field with a unique integration of research, education, innovation and public / private government service. DTU Wind and Energy Systems has extensive expertise in wind turbine technology, focusing on the impact of loads, structural design and reliability, aeroelastic design and materials.

Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear mission to develop and create value using science and engineering to benefit society. That mission lives on today. DTU has 13,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.