Job description
IntelliWind is a Marie Sklodowska-Curie Doctoral Network funded by the Horizon Europe program. The project aims to train 16 highly motivated and exceptional PhD candidates. Its primary research objective is to reduce human involvement in decision-making and minimize the need for direct human interventions in operations and maintenance processes. This approach allows human resources to focus on more complex, better-planned, and efficient operations, leading to significant improvements in cost efficiency and reduced labor intensity in wind farm operations. The project will catalyze a shift in the skills and tasks required for wind power plant operations, moving from traditional engineering roles to the design, analysis, and interaction with automated machine algorithms.

Doctoral candidate position DC6 will be undertaken within the Intelligent Sustainable Prognostics (iSP) Group at the Faculty of Aerospace Engineering, Delft University of Technology, with collaborative placements at the Fraunhofer Institute for Wind Energy Systems IWES and the Technical University of Denmark.

The candidate's research will focus on developing a self-learning, prognostics-based blade load control methodology. This methodology will adapt blade loads to maintain blade lifetime consumption at a target level, given a predefined reliability threshold. The objectives of this thesis include:

• Developing an interpretable prognostic model along with an uncertainty management strategy to estimate the conditional reliability and Remaining Useful Life (RUL) of wind turbine blades, considering historical data, expert knowledge, environmental conditions, and operational stress.
• Designing a self-learning control algorithm that adjusts blade loading in real-time based on the predicted RUL, conditional reliability, and current health state to optimize energy output and reduce maintenance requirements.
• Experimentally validating the developed control algorithm, measuring real-time energy output adaptations to optimize cost functions.

Requirements
Applicant Requirements:

• Educational Background: An MSc degree (or equivalent) in engineering, mathematics, or a related field, with a strong background in machine learning, stochastic modeling, and Bayesian statistics. (Note: You may apply before obtaining your master’s degree, but you must have it before starting the position.)
• Programming Skills: Proficiency in programming languages such as Python, C, or R.
• Teamwork and Responsibility: Ability to work effectively within a project team and take responsibility for your own research objectives.
• Communication Skills: Excellent communication skills in English, both written and oral.

Eligibility Criteria:

• You must not have been awarded a PhD. Applicants who have successfully defended their doctoral thesis but have not yet formally received the doctoral degree are not eligible.
• You must not have resided or carried out your main activity in the Netherlands for more than 12 months within the last 3 years.

The selected candidate is expected to contribute to a dynamic and collaborative atmosphere within the Intelligent Sustainable Prognostics (iSP) Group for Operations & Maintenance, Department of Aerospace Structures and Materials.     

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Aerospace Engineering
The Faculty of Aerospace Engineering at Delft University of Technology is one of the world’s most highly ranked (and most comprehensive) research, education and innovation communities devoted entirely to aerospace engineering. More than 200 science staff, around 270 PhD candidates and close to 3000 BSc and MSc students apply aerospace engineering disciplines to address the global societal challenges that threaten us today, climate change without doubt being the most important. Our focal subjects: sustainable aerospace, big data and artificial intelligence, bio-inspired engineering and smart instruments and systems. Working at the faculty means working together. With partners in other faculties, knowledge institutes, governments and industry, both aerospace and non-aerospace. Working in field labs and innovation hubs on our university campus and beyond.

Click here to go to the website of the Faculty of Aerospace Engineering.

Conditions of employment
Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2872 per month in the first year to € 3670 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

For international applicants, TU Delft has the Coming to Delft Service. This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme for partners and they organise events to expand your (social) network.

Additional information
For more information about this vacancy, please contact Dr. Nick Eleftheroglou, Assistant Professor, via email: N.Eleftheroglou@tudelft.nl.

Application procedure
Are you interested in this vacancy? Please apply no later than 31 October 2024 via the application button and upload (in English):

• A cover letter explaining the motivation for applying to this position, including a reflection on the aforementioned requirements.
• A Curriculum vitae.

Interviews for selected candidates are expected to take place in November 2024. The expected starting date of the PhD project is January 2025 (though flexible if needed).

Please note:

• You can only apply online. We will not process applications sent by email.
• A pre-employment screening can be part of the selection procedure.
• Applying for an exemption for specific research and educational areas is an obligatory part of the selection procedure for this vacancy. This exemption must be obtained from the Ministry of Education, Culture and Science (OCW) before an employment contract is agreed upon. Click here for more information.
• You can apply online. We will not process applications sent by email and/or post.
• Please do not contact us for unsolicited services.