KEY TAKEAWAYS

• 
  Hours
  38 hr.
• 
  Salary indication
  Salary gross/monthly
  based on full-time
  € 3,059 - € 3,881
• 
  Deadline
  30 Sep 2025
  In recent years, the energy sector has undergone changes that have a high impact on the dynamics in power markets. One of the changes has been the shift towards energy production from intermittent sources, such as wind and solar. As a consequence of this shift, the amounts of energy that are traded at the short-term markets throughout a day are uncertain, as they depend on hardly predictable weather conditions.
  This uncertainty increases the volatility of short-term energy prices, and thus makes it much more challenging to make economically viable energy trading decisions. One way to respond to this challenge is to leverage assets such as grid-level battery storage, and electrolyzers to have more flexibility when making trading decisions. The challenge then is how to optimally leverage such an asset to make viable trading decisions under high price volatility.
  This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with these challenges. By leveraging real-time data, developed algorithms continuously adapt to market dynamics and respond to changing market signals with economically viable trading decisions.
  Within this project, you will have the opportunity to work not only with colleagues at the HBE department of the University of Twente, but also with researchers and energy traders from our industrial partner in the energy sector, including the opportunity for regular visits to our partner’s trading floor.

Please refer the Job description for details