The project runs over four years starting from January 2023 whereby the candidate will be employed by YUSO, yet financed exclusively via the MCS project ADOreD during the first three years. The objective is that the candidate completes and defends her/his Ph.D. in the fourth year.
Conditions apply to candidates applying for a MCS fellowship such as this. You can check your status via the website:
This industrial PhD project provides the unique opportunity to obtain and improve your academic skills on the one hand, and to strengthen your industrial experience by partly being embedded in a commercial organization to apply your research skills. Additionally, the project will provide the opportunity to interact with researchers from other universities and industrial partners, working on complementary research topics in the framework of ADOreD.
The selected candidate will be integrated in the YUSO team of quantitative analysts, consisting of highly experienced professionals with a strong drive to outperform within the energy sector through solid research and exquisite portfolio management. The team is responsible for the performance of portfolio strategies and hence contributes directly to YUSO’s net revenue, while providing solid staging ground for your research and Ph.D. Within KU Leuven, the selected candidate will be part of the Electa research group as part of the power group consisting of 30+ researchers working on the field of HVDC grids and decision support tools for grid operators with an affinity on power system modelling and optimisation.
- Develop, monitor and manage risk based models for the energy markets, including models for Offshore wind and AC/DC InterConnector(IC) technologies
- Improve existing models by fine-tuning them to new geographical markets, technologies or market conditions when possible
- Use a variety of statistical and applied mathematical methods such as Machine Learning or Artificial Intelligence
- Act as the main point of contact on aspects related to ADOreD and in general offshore networks within YUSO
- Analyse timeseries data across a range of energy markets using a variety of statistical and applied mathematical methods to generate reliable input data for the developed models
- Write internal reports and give presentations summarising methods, results and performance
- Maintain a self-critical yet convincing attitude towards your colleagues and clientsAuthor publications in scientific journals and international conferences as part of the PhD requirements
- Masters in computer science, electrical engineering or equivalent statistical /mathematical / scientific / econometric discipline
- Candidates must abide by conditions for MCS Ph.D. fellowships as described via the link https://change-itn.eu/vacancies/eligibility-criteria/
- Willing to work towards a Ph.D. hosted by KU Leuven in the framework of the MCS ADOreD project
- Experience in energy markets and/or High Voltage engineering through your education is a plus
- Understanding of the main economic fundamentals driving energy markets
- Experience of statistical or mathematical modelling and data analysis
- Knowledge of mathematical optimisation techniques is a plus
- Experience with one of Matlab, Julia, R and preferably Python
- Excellent communication skills with the ability to influence and persuade
- This vacancy is open to Dutch and/or English speaking applicants
- Willingness to travel and present research results during conferences or related events
- Team player
You will join a team of dynamic and young professionals, passionate about the integration of renewable energy flows into the energy markets YUSO is active in as well as the ELECTA research group within KU Leuven. Given the hybrid nature of employment as a Ph.D. candidate, your workload and location will be divided 50-50% between KU Leuven and YUSO.
This job opening includes remote work and/or working from the YUSO offices in Waregem, Belgium and the KU Leuven offices in Leuven and Genk - Campus EnergyVille offering a multitude of multimedia to connect with other colleagues remotely.