PhD Student -Engineer with desirable specialization in Wind Energy (DC9); knowledge and experie[...] Lisboa

PhD Student -Engineer with desirable specialization in Wind Energy (DC9); knowledge and experie[...]
Lisboa
Lisboa, Lisboa, Portugal

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Ph
D Student - Engineer with desirable specialization in Wind Energy (DC9); knowledge and experience, Lisboa

Client:

Location: Lisboa, Portugal

Job Category: Other

Salary: -$0-0/monthly

EU work permit required: Yes

Job Views: 7

Posted: 22. 01. 2025

Expiry Date: 08. 03. 2025

Job 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 digitalisation 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
- 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
- level training in the new emerging research field of Wind Energy Data Science and Digitalisation.

An outstanding
-
- innovation programme, and a unique training programme that combines
- 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
-
-
- 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
- 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
- value products or services with the guidance from established researchers and entrepreneurs. As a result, a
-
- 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
- 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
- conscious power curtailment. Leveraging machine learning and digital twin technologies, candidates will develop innovative,
- 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
- making (of which candidates will contribute to this cause).

Candidates will also design and implement digital twin frameworks that integrate
- time data for continuous health monitoring of wind turbines and their components. This proactive monitoring will contribute to optimizing turbine performance while ensuring
- term reliability.

Additionally, the role involves developing probabilistic models to assess turbine fatigue and predict their remaining operational lifespan. By blending
- based and
- 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
- edge developments in wind energy optimization.

Secondments:

Two academic secondments. UNIZAR for training and collaboration on data science and completing mandatory Ph
D courses (Prof. Julio J. Melero). TU-DELFT for joint work on
- driven Digital Twins (Prof. Simon J. Watson).

Personal Supervisory Team:

Co-Supervisors: Julio J. Melero (UNIZAR) and Simon J. Matson (TU-Delft)

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 Ph
  D in the first quarter of 2025.

Specific requirements:

• Excellent writing and communication skills in English
• Useful skills to have or have knowledge in but not required:
• Programming Languages: Python
• Testing Frameworks: pytest, unittest
• Version Control: Git, Git
  Lab
• 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 Ph
  D program at University of Zaragoza.

You will work under a 36-month employment contract with 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:

Please see Euraxess profile for additional information on benefits.