Key takeaways

The PhD research will be performed as part of an industry funded project on algorithmic energy trading. The goal of this project is to conceive, develop, and evaluate self-learning algorithms that are able to act autonomously on short-term power markets. The algorithms need to calculate intelligent trading decisions (buying and selling energy) in real time while being exposed to a stochastic and dynamic market environment. Designing such algorithms requires working at the intersection of statistical learning and mathematical optimization. Evaluating such algorithms requires close interaction with our industrial project partner from the energy sector. Concretely, the PhD candidate will perform research on understanding how self-learning algorithms can facilitate automated trading on short-term power markets.

The challenge

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.

We look for a highly motivated researcher who is driven by curiosity and has/is:

• Master’s degree or equivalent in Computer Science, Operations Research, Mathematics, Industrial Engineering, or related discipline;
• Affinity and/or experience with computer programming, statistical learning, and optimization techniques;
• A good team spirit and feel at home at the intersection of academia and industry;
• Able to do independent research within an ongoing research project;
• Willing to develop skills in coding, writing, and publishing;
• Exhibit a strong passion and possess outstanding skills in algorithmic design;
• Possess good communication skills and an excellent command of English.

We encourage high responsibility and independence, while collaborating with colleagues, researchers, other university staff and industry partners. We follow the terms of employment by the Dutch Collective Labour Agreement for Universities (CAO). Our offer contains: a fulltime 4-year PhD position with a qualifier in the first year; excellent mentorship in a stimulating research environment with excellent facilities; and a personal development program within the Twente Graduate School. It also includes:

• Gross monthly salary of € 2.770 in the first year, increasing each year up to € 3.539 in the fourth year;
• Excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
• 29 holidays per year in case of full-time employment;
• A training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
• A green campus with free access to sports facilities and an international scientific community;
• A family-friendly institution that offers parental leave (both paid and unpaid);
• A full status as an employee at the UT, including pension, health care benefits and good secondary conditions are part of our collective labour agreement CAO-NU for Dutch universities.

The HBE cluster is dedicated to encouraging a supportive and inclusive working culture. Our aim is that all job applicants are given equal opportunities. When we select candidates for employment, it will be on the basis of their competence and ability. To support the workforce diversity, we are open to offer flexible working conditions on an individual basis to support work-life balance, that may include contract of employment, working hours and location, or child care arrangements.

Please have a look at our video to learn more about our department.

The High Business Entrepreneurship corporate video can be watched via this link.