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MSCA Doctoral Network PhD student position (100%)
The Chair of Structural Mechanics and Monitoring at ETH Zurich is seeking a prospective PhD student with expertise in areas such as knowledge engineering, knowledge management, knowledge grounding, or graph databases, and a strong interest in wind energy.
Keywords: Knowledge Engineering, Knowledge Graph, Large Language Model (LLM), Knowledge Grounding, Retrieval Augmented Generation, Knowledge Discovery, Neuro-symbolic AI, Semantic Web
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.
The “Novel methods for a knowledge graph generation approach within TWEED’s Virtual Wind Farm Hub” project is led by **Dr. Sarah Barber** at Eastern Switzerland University of Applied Sciences (OST), who will also act as a main supervisor.
Due to its status as a Swiss University of Applied Sciences, OST cannot matriculate or award PhD degrees. **The PhD student will therefore be matriculated at ETH Zurich under Prof. Eleni Chatzi (Chair of Structural Mechanics and Monitoring), who will co-supervise the thesis.**
**Required skills and qualifications:**
- Completed Master’s degree (at the date of appointment) in a technical subject such as mechanical engineering, electrical engineering, or computer science, or equivalent
- 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.
- Experience of and understanding of basic knowledge engineering concepts, including knowledge graphs, semantic web, ontologies and semantic artefacts
- Experience with programming languages such as Python, Javascript and Java
- Interest in and ability to work on interdisciplinary projects with a range of different people from outside your domain, especially at the interface between computer science and engineering
- Excellent writing and communication skills in English
- Basic communication skills in German, or willingness to learn
- 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)
- Fulfill the requirements for admission to the PhD program at the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich described here: https://baug.ethz.ch/en/doctorate/after-2022.html
- Optional: Experience with tools such as Spring, Node.js, Angular, React, Mongo DB, Elasticsearch or TensorFlow
- Optional: Experience in wind energy.
**Eligibility criteria:**
- At the time of recruitment, applicants must not hold a doctoral degree or equivalent.
- 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. Additional restrictions may apply to the nationals of the countries which require a security screening as per ETH / OST policies.
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.
The “Novel methods for a knowledge graph generation approach within TWEED’s Virtual Wind Farm Hub” project is led by **Dr. Sarah Barber** at Eastern Switzerland University of Applied Sciences (OST), who will also act as a main supervisor.
Due to its status as a Swiss University of Applied Sciences, OST cannot matriculate or award PhD degrees. **The PhD student will therefore be matriculated at ETH Zurich under Prof. Eleni Chatzi (Chair of Structural Mechanics and Monitoring), who will co-supervise the thesis.**
**Required skills and qualifications:**
- Completed Master’s degree (at the date of appointment) in a technical subject such as mechanical engineering, electrical engineering, or computer science, or equivalent - 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. - Experience of and understanding of basic knowledge engineering concepts, including knowledge graphs, semantic web, ontologies and semantic artefacts - Experience with programming languages such as Python, Javascript and Java - Interest in and ability to work on interdisciplinary projects with a range of different people from outside your domain, especially at the interface between computer science and engineering - Excellent writing and communication skills in English - Basic communication skills in German, or willingness to learn - 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) - Fulfill the requirements for admission to the PhD program at the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich described here: https://baug.ethz.ch/en/doctorate/after-2022.html - Optional: Experience with tools such as Spring, Node.js, Angular, React, Mongo DB, Elasticsearch or TensorFlow - Optional: Experience in wind energy.
**Eligibility criteria:**
- At the time of recruitment, applicants must not hold a doctoral degree or equivalent. - 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. Additional restrictions may apply to the nationals of the countries which require a security screening as per ETH / OST policies.
The goal of this DC project, titled “Novel methods for a knowledge graph generation approach within TWEED’s Virtual Wind Farm Hub”, is to investigate, design and implement a knowledge graph generation approach for the Virtual Wind Farm Hub within the TWEED project. The Virtual Wind Farm Hub will be an open platform to facilitate the exchange of datasets, research results and applications and allow domain experts and data scientists to prototype new services.
The goal of this DC project, titled “Novel methods for a knowledge graph generation approach within TWEED’s Virtual Wind Farm Hub”, is to investigate, design and implement a knowledge graph generation approach for the Virtual Wind Farm Hub within the TWEED project. The Virtual Wind Farm Hub will be an open platform to facilitate the exchange of datasets, research results and applications and allow domain experts and data scientists to prototype new services.
Apply for the position through the OST's Job portal:
https://jobs-ost.ch/MSCA-Doctoral-Network-PhD-student-100-de-j859.html
Apply for the position through the OST's Job portal:
