PhD scholarship - AI augmented design optimization of wind farms - DTU Wind

Are you a curious, self-driven individual with a strong passion for developing sustainable energy solutions? The Department of Wind and Energy Systems at the Technical University of Denmark (DTU) invites applications for a PhD position focused on the topic of AI augmented design optimization of wind farms at DTU Wind. This is your opportunity to make a significant contribution towards the next generation wind farm design methodology.

Job Information

• Organisation/Company: Technical University of Denmark (DTU), Department of Wind and Energy Systems
• Research Field: Wind Energy, Computer Science, Data Science
• Researcher Profile: First Stage Researcher (R1)
• Country: Denmark
• Application Deadline: November 30th
• Type of contract: Temporary (3 years)
• Job Status: Full Time
• Offer Starting Date (Vacancy Opening): November 1st
• Is the job funded through the EU Research Framework Programme?: YES
• Marie Curie Grant Agreement Number: 101168673
• Is the Job related to staff position within a Research Infrastructure?: NO

Offer description

TWEED Project
TWEED is looking for 12 talented and motivated Doctoral Candidates (DCs) with the skills, knowledge and enthusiasm to work as part of a network to advance the field of digitalistion within the wind energy sector.

The “Training Wind Energy Experts on Digitalisation (TWEED)” Doctoral Network (DN) aims to train the next generation of excellent researchers equipped with a full set of technical and complementary skills to develop high-impact careers in wind energy digitalisation.

Co-funded by the European Commission through the Horizon Europe Marie Sklodowska Curie Doctoral Networks Programme, the TWEED network offers 12 Doctoral Candidates (DCs) positions to provide high-level training in the new emerging research field of Wind Energy Data Science and Digitalisation. An outstanding research-for-innovation programme, and a unique training programme that combines hands-on research training, interactive schools and hackathons, innovation management and placements with industry partner organisations has been designed for the DCs who will participate in the network. Alongside the exciting research topics related to wind energy data science, the research programme also includes state-of-the-art technology to develop a new Wind Energy Data Science Hub that will facilitate a virtual research environment to foster collaboration, data sharing and testing of innovative solutions to significantly increase the value of wind energy.

The network will provide an interdisciplinary and inter-sectoral context to foster creativity in tackling wind energy data science and digitalisation challenges by developing solutions for commercial exploitation. DCs will be trained in business innovation to extend their focus beyond the academic context, to be able to identify added-value products or services with the guidance from established researchers and entrepreneurs. As a result, a research-for-innovation mindset will be developed to provide enhanced career prospects for the fellows, equipping them with a complete set of thematic, technological and innovation skills.

DCs are expected to i) conduct high quality, original academic research in the fields of Wind Energy, Digitalisation, Data Science and Computer Science, ii) participate in the network’s planned training-dissemination activities and mobility plan, iii) collaborate with fellow researchers, with the goal of advancing and promoting the network's objectives.

The most talented and motivated candidates will be selected to participate in the network's interdisciplinary collaborative research training, preferably starting in February 2024. The assessment shall be carried out by the TWEED recruitment team.

DC Project
Internal code of the position: DC1

Host Institution: Technical University of Denmark (DTU), Department of Wind and Energy Systems
Brief description: AI augmented design optimization of wind farms

The overall design of large wind farms concerns the selection of turbines, siting of turbine layout and many other engineering decisions. Its optimization holds a great potential for reducing the investment costs and increasing the profit. Traditionally, wind farm layouts are optimised with physics-based flow models and search & evolutionary algorithms such as Random Search and Genetic Algorithm, which are also a type of AI techniques for solving optimization problems. Many studies have applied machine learning (ML), a subset of AI techniques, to build surrogate models for wind farm flows. This project will investigate the potential of combining the strengths of ML based surrogate modelling with AI enabled search & evolutionary algorithms, by proposing a framework/ methodology to better integrate AI into the workflow of design optimization of wind farms to achieve faster and better results. The process of building/refining the surrogate model will be integrated with the searching/optimization process guided by the search & evolutionary algorithms to save computational costs and improve optimization results. The research will consider both onshore and offshore applications, with realistic modelling of wind farm costs included. A new framework/methodology to integrate AI into the workflow of design optimization of wind farms, which can be generalised to other engineering design problems, will be developed, that can better reduce investment costs and improve profits for future wind farms.

Secondments:

• 3 months industrial secondment at EDF UK hosted by Suguang Dou to improve the cost model of wind farms, with a tentative date of May-July in 2026.
• 3 months academic secondment at TU-Delft hosted by Prof. Simon Watson to combine the wind resource assessment techniques developed by DC2 and apply the optimization framework to multiple wind farm design, with a tentative date of Feb-April in 2027.

Personal Supervisory Team: Ju Feng (DTU), Pierre-Elouan Mikael Réthoré (DTU), Prof. Simon Watson (TU-Delft), Suguang Dou (EDF UK)

Requirements
Research Field: Engineering, Wind Energy, Data Science, Computer Science
Education Level: Master Degree or equivalent
Skills / Qualifications:

• Applicants must be proficient in the English language.
• Master degree or equivalent obtained by the time they are appointed. Students currently in the final year of a Master’s degree are encouraged to apply but should note that if selected, they will be expected to start their PhD in the first quarter of 2025.

Specific requirements:

• Programming skills
• Ability to work in a team and independently.
• Willingness to follow the mobility plan of the programme (conduct secondments in the country of the host institute or abroad)
• The successful candidate must also fulfill the requirements for admission to a PhD program at DTU as described at: https://www.dtu.dk/english/education/phd/applicant/pre_acceptance-1-

Languages: English
Level: Excellent

Salary and appointment terms
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 3 years.

Eligibility criteria
All applicants must, at the date of the recruitment, comply with the following ELIGIBILITY CRITERIA:

• Candidate status: At the time of recruitment, applicants must not hold a doctoral degree or equivalent.
• Mobility Rule: Applicants can be of any nationality. However, applicants must not have resided or carried out their main activity (work, studies, etc.) in the country of the recruiting organisation for more than 12 months in the 3 years immediately before the appointment. This excludes short stays such as holidays or compulsory national service

Candidates are required to document in their applications their compliance with the eligibility criteria. To prove their eligibility, candidates can use supporting documents such as studies, residence or work certificates.

How to apply
To apply, please read the full job advertisement here: PhD scholarship - AI augmented design optimization of wind farms - DTU Wind - https://efzu.fa.em2.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/4321/?utm_medium=jobshare

Application deadline: 30 November 2024 (23:59 Danish time) .

Contact
Main supervisor: Senior Researcher, Ju Feng, +45 93510691, [email protected]
Main contact of the project: [email protected]