ANNEA
PhD Student – Engineer with desirable specialization in Wind Energy (DC9); knowledge and experience in computer science and/or data science are welcome
Job Description
Your mission
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: DC9, Start: M13,M28
Host Institution: ANNEA.ai
Brief description of the project:
The doctoral candidate will focus on advanced operational and maintenance (O&M) strategies for wind turbines with a specific emphasis on predictive maintenance and lifetime-conscious power curtailment. Leveraging machine learning and digital twin technologies, candidates will develop innovative, data-driven approaches to enhance turbine performance and reliability.
One key area of focus is creating explainable frameworks for predictive maintenance, utilizing SCADA data to identify potential component failures. This will enable more efficient scheduling of maintenance, reducing unplanned downtime and improving operational decision-making (of which candidates will contribute to this cause).
Candidates will also design and implement digital twin frameworks that integrate real-time data for continuous health monitoring of wind turbines and their components. This proactive monitoring will contribute to optimizing turbine performance while ensuring long-term reliability.
Additionally, the role involves developing probabilistic models to assess turbine fatigue and predict their remaining operational lifespan. By blending physics-based and data-driven techniques, candidates will support efforts to extend the turbines’ operational life, contributing to the sustainability of wind energy systems.
As part of their work, candidates will collaborate on a joint research paper to present the findings and outcomes of these innovative projects, contributing to cutting-edge developments in wind energy optimization.
Secondments:
Two academic secondments. UNIZAR for training and collaboration on data science and completing mandatory PhD courses (Prof. Julio J. Melero). TU-DELFT for joint work on data-driven Digital Twins (Prof. Simon J. Watson).
Personal Supervisory Team:
Main Supervisor: Dr. Maik Reder
Co-Supervisors: Julio J. Melero (UNIZAR) and Simon J. Matson (TU-Delft)
Your profile
Research Field: Engineer with desirable specialization in Wind Energy (DC9); knowledge and experience in computer science and/or data science are welcome
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:
- Excellent writing and communication skills in English
- Useful skills to have or have knowldge in but not required:
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- Programming Languages: Python
- Testing Frameworks: pytest, unittest
- Databases: Timescale (PostgreSQL), MongoDB
- DevOps: AWS, Kubernetes, Docker
- Version Control: Git, GitLab
- 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 University of Zaragoza.
Languages: English Level: Excellent
Why us?
Benefits
You will work under a 36-month employment contract with the competitive conditions and salary adapted to the living costs in each host country, set by the MSCA Doctoral Networks (DN). The MSCA DN programme offers a highly competitive and attractive salary and working conditions. The successful candidates will receive a salary in accordance with the MSCA regulations for DCs, according to the national rules of the country with full social security benefits.
The successful candidate will receive a financial package plus an additional mobility and family allowance according to the rules for Doctoral Candidates (DCs) in an EU Marie Skłodowska-Curie Actions Doctoral Networks:
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The gross salary will be calculated by deducting the applicable employer taxes and social security contribution for each country, will be approximately €3,059 (without family allowance) or €3,592 (with family allowance) / month. Additional deductions may apply based on your personal circumstances and local tax/social security regulations.
In support of families with young children, flexible working hours will be offered to the DC whenever it is feasible within the requirements of the project.
Following the EU’s commitment to DEI, the TWEED network and ANNEA.ai encourages and promotes the participation of under-represented groups such as women in technical careers, people from diverse economic and ethnic backgrounds, people with disabilities, those who identify as neurodivergent and LGBTQA+. The {Host institution} community aims to exercise a policy of equal opportunities at all times.
Additional information can be found in Information Note for Marie Sklodowska-Curie fellows in Doctoral Networks.
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.
If you would like to apply please apply through our portal and you will be contacted for additional information! Please submit all information and documentation in English.
