PhD position: Learning from Observations and Experts for facilitating Decision Support for Wind Energy Structures

IntelliWind seeks to appoint 16 highly motivated Ph.D. candidates to a range of positions across Europe. These Doctoral Candidate (DC) positions offer an exciting opportunity to work with leading experts across Europe to develop Intelligent systems for autonomous wind power plant operations via the IntelliWind MSCA Doctoral Network funded by the European Commission’s Horizon 2023 Marie Sklodowska-Curie programme.

The scientific aim of IntelliWind is to reduce the role of humans in the decision process, as well as reducing the need for direct human interventions in the operations and maintenance process for wind energy structures. This frees up human resources to carry out more elaborate, better planned, and more efficient operations – leading to significant improvements in cost efficiency (3-5% lower Levelized Cost of Energy), and reduced labour-intensiveness of wind farm operations. The project will trigger a change in thinking in the skills and tasks that are being used in Wind Power Plant operations from classical engineering tasks to designing, analysing, and interacting with automatic machine algorithms. The IntelliWind network consists of leading academic institutions and leading industry partners from eight different countries (Belgium, Denmark, Germany, Spain, Netherlands, Portugal, France and Switzerland).

In this role, you will benefit from advanced training in various European universities and industry, participate in scientific discussions within the rich context of the Marie Sklodowska-Curie Action Doctoral Network (MSCA DN) "Intelligent systems for autonomous Wind power plant operations" (IntelliWind).

Doctoral candidate position A1, in particular, will be hosted and matriculated at ETH Zurich, with secondments at Ramboll, RTDT Laboratories AG and the Technical University of Munich. The candidate will focus on imitation learning from observations and expert inputs for planning of fault reaction strategies. The objectives of this thesis include

• development of decision support tools, such as tools relying Partially Observable Markov Decision Processes (POMDPs) capitalizing on use of Machine Learning (ML) models; and
• fusion of monitoring data and expert judgement into a decision support tool for automating mitigation actions.

This doctoral student position is offered for 4 years with the earliest starting date in October 2024. The primary academic advisor is Prof. Eleni Chatzi (ETH Zurich). Prof. Daniel Straub (TUM) will serve as second advisor.

Applicants must hold a M.Sc.Diploma (120 ECTS points) or equivalent in civil, mechanical or electrical engineering, geosciences, physics, applied mathematics, computer sciences or related fields, and be at the beginning of their research career. Principal qualifications include strong analytical and quantitative skills in numerical analysis, programming, high-performance computing, as well as skills in dynamics & control, data analysis and modelling, and interest in laboratory-based experimentation and engineering applications. A solid knowledge of English as the spoken and written language of work is mandatory. We expect good interpersonal skills, the ability to thrive in a diverse, multidisciplinary environment, ability to present work in international conference, as well as the willingness to spend a number of months working with project collaborators at partner universities in this project.

Eligibility Criteria: Researchers can be of any nationality. At the time of recruitment, researchers

• Should not have been awarded a title of PhD (Applicants who have successfully defended their doctoral thesis but not yet formally been awarded the doctoral degree will not be considered eligible.)
• Should not have resided or carried out your main activity in Switzerland, for more than 12 months within the last 3 years

We offer you the opportunity to be a part of IntelliWind, which will not only facilitate sixteen DCs in reaching a high level of technical and project-specific excellence but will also provide you with many opportunities for developing skills that are transferable to a broader landscape of opportunities. You will have the opportunity to visit industry and other academic institutions within the consortium. After completing the program, you will have a thorough understanding of the process from research via innovation to industry implementation and a strong career-defining network. As position A1 is for ETH doctoral students, ETH immatriculation and employment rules apply.

The IntelliWind Doctoral Network provides training for a new creative, entrepreneurial, innovative, and resilient industry-oriented academic generation ready to face current and future challenges towards reducing the role of humans in the decision process and the need for direct human interventions in the wind power plant operations and maintenance activities. The trained Doctoral Candidates will be able to convert knowledge and ideas into new products, and services for economic and social benefit.