PhD in Artificial Intelligence for power grid balancing using recommendation-enhanced Demand Response
PhD in Artificial Intelligence for power grid balancing using recommendation-enhanced Demand Response
PhD in Artificial Intelligence for power grid balancing using recommendation-enhanced Demand Response
Published | Deadline | Location |
---|---|---|
today | 20 Oct | 's-Hertogenbosch |
JADS is seeking enthusiastic colleagues for the position of PhD students. We operationalize the huge ambition around AI by explicitly aligning our research agenda on Robust AI with the United Nation’s sustainable development goals.
The project is funded in a public private partnership by NWO/NLAIC and the private partners. This position is part of the LaNubia project.
Job description
Jheronimus Academy of Data Science (JADS) Den Bosch, is proud to start with three large Robust AI labs together with:
Short Description
Are you fascinated in how we can align human behavior with smart AI technologies to achieve a sustainable energy transition? In this project we aim to develop novel recommendation technology that helps energy consumers in managing their energy consumption (and production). Current methods assume users passively follow the demand-response (pricing) schemes. AI technology such as recommender algorithms could play an active role in recommending consumers how and when to distribute their energy usage and return.
Job Description
As part of the Ilustre lab, you will develop recommendation technologies to tailor advice on energy usage decisions in the smart grid. Demand-Response methods motivates energy consumers in some way (e.g. pricing-based) to adjust their energy usage to the available energy resources and demand. Smart grid technology allows DR to be more data-driven and a multitude of AI technologies have already been applied to DR but the consumer side of has not been developed much. However, modeling and supporting the energy decisions of users is crucial, as a smart grid will only be successful if the users of the systems behave predictable and in the most energy efficient way. For example, charging your electric car when the solar PV generated in the neighborhood is highest will put the smallest load on the grid and will be cheapest, but can only be achieved if it fits in the users’ calendar and daily habits.
In this project you will extend current AI energy consumption forecasting and DR methods with tailored and explainable recommendations that will provide both household and industry consumers with tailored advice how to adjust for Demand Response. As many stakeholders are involved, and dynamic changes in energy usage will affect other consumers in the network, a multi-stakeholder perspective on recommendations should be employed. You will work together with other PhD students in the lab that will work on forecasting and on identifying the stakeholders.
In the project you will review existing methods for AI in DR, work on recommender model and approaches, test several of these models in prototypes and in a pilot project within the Ilustre lab.
Specifications
- 32—40 hours per week
- €2541—€3247 per month
- 's-Hertogenbosch View on Google Maps
Tilburg University
Requirements
- A master’s degree (or an equivalent university degree) in computer science, data science, AI or related, with a strong background in machine learning and recommender systems. Affinity with human-computer interaction methods and interest in human cognition and decision making is a plus.
- A research-oriented attitude.
- Ability to work in a team and interest in collaborating with the industrial partners.
- Ability to communicate and translate the research results to practice.
- Academic curiosity and an interest to collaborate in a multidisciplinary context.
- Fluent in spoken and written English (C1 level).
Conditions of employment
Being appointed at JADS provides a meaningful job in a dynamic and ambitious community of 2 universities and startups. Based on the project, the candidate receives an appointment at TU/Eindhoven or Tilburg University.
As a PhD Student at JADS you will have a full-time employment for a total of four years, with an intermediate evaluation after nine months. JADS offers excellent employment conditions with attention to flexibility and (personal) development and attractive fringe benefits. The gross monthly salary is in accordance with the Collective Labor Agreement for Dutch Universities. The minimum gross salary is € 2.541 per month up to a maximum of € 3.247 in the fourth year;
You are entitled to a vacation allowance of 8% and a year-end bonus of 8.3% of your gross annual income. If you work 40 hours per week, you will receive 41 paid days of leave per year. PhD Students from outside the Netherlands may qualify for a tax-free allowance of 30% of their taxable salary if they meet the relevant conditions. The university applies for this allowance on their behalf. JADS will provide assistance in finding suitable accommodation.
Preferred starting date: January 1, 2023.
To develop your teaching skills, you will spend 10% of your employment on teaching tasks. To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students.
Next to that we offer all kinds of facilities and arrangements to maintain an optimal balance between work and private life. All employees of the university are covered by the so-called General Pension Fund for Public Employers (Stichting Pensioenfonds ABP).
JADS values an open and inclusive culture. We embrace diversity and encourage the mutual integration of groups of employees and students. We focus on creating equal opportunities for all our employees and students so that everyone feels at home in our university community.
All researchers working in JADS have contracts at either Tilburg University or Eindhoven University of Technology. Please visit Working at Tilburg University and Working at Eindhoven University of Technology for more information on their respective employment conditions.
Employer
JADS
We do cool stuff that matters, with data. The Jheronimus Academy of Data Science (JADS) is a unique cooperation between Eindhoven University of Technology (TU/e) and Tilburg University (TiU). At JADS, we believe that data science can provide answers to society’s complex issues. We provide innovative educational programs, data science research, and support for business and society. With a team of lecturers, students, scientists and entrepreneurs - from a wide range of sectors and disciplines – we work on creating impact with data science. We do this by connecting people, sectors and industries: in the past 5 years we have been working with 300+ organizations on data-related projects. Our main drivers? Doing cool stuff that matters with data. Our location at the former monastery Mariënburg in Den Bosch houses a vibrant campus fully dedicated to data science.
Additional information
More information
Do you recognize yourself in this profile and would you like to know more? Please contact dr. Martijn Willemsen ([email protected] or or dr. Claudia Zucca ([email protected]).
For information about terms of employment, please contact Marielle van Gerven, HR Advisor, [email protected] or +31 40 247 3699.
Working at Tilburg University
At Tilburg University, we seek to study and understand society and in this way we contribute to solving complex societal issues. Our core values are: curious, Caring, Connected, and Courageous.
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Application procedure
We invite you to submit a complete application by using the 'apply now'-button on this page. The application should include a:
- Cover letter in which you describe your motivation and qualifications for the position.
- Curriculum vitae, including a list of your publications and the contact information of three references.
- Brief description of your MSc thesis.
We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.
Note: CHECK OUT THE OTHER PhD vacancies at JADS!
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