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 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: DC12

Host Institution: ANNEA.ai

Brief description of the project: 

The doctoral candidate will be responsible for developing a unified technological platform that integrates advancements across various wind energy research areas. This includes analyzing technological patterns from previous work to propose cohesive solutions that align with ongoing projects. A major focus will be the creation of a comprehensive Wind Energy Knowledge Hub, featuring a source code repository, data lake, knowledge models, and training resources. The hub will be designed according to European Open Science Cloud (EOSC) guidelines, enabling data reuse and innovation in wind energy research. Candidates will also develop semantic artefacts for data integration and implement a novel knowledge graph generation approach for effective data management.

In this role, candidates will facilitate global collaboration within the wind energy community by promoting knowledge sharing through the WeDoWind Framework. They will design a reference model for the knowledge hub, which will serve as both a deployment proposal and a training resource. Additionally, candidates will define the data and knowledge resources needed by researchers in the field, drawing on solutions from other industries to develop a modern, scalable implementation framework aligned with international standards.

Candidates will also contribute to industry-wide advancements in data sharing and standardization by co-authoring a joint paper addressing the challenges of wind energy data interoperability. Their work will help promote greater collaboration and progress in wind energy research, supporting long-term sustainability efforts across the sector.

Secondments: 

3 months in UNIZAR to attend PhD courses and coordinate research activities (F. Javier Zarazaga-Soria). 5,5 months distributed into 2 weeks secondment to the host places of Doctoral Candidates 1-11 to understand the pilots developed and to support their implementation in the Hub platform.

Personal Supervisory Team: 

Main Supervisor: Maik Reder

Co-Supervisors: F. Javier Zarazaga-Soria (UNIZAR)