Enabling the social change towards adoption of energy flexibility services
The energy market is experiencing great transformations. One of these is the emergence of new energy services that demand a more engaged role of consumers and their participation in a multi-actor ecosystem: flexibilility services. Despite the economic, environmental and societal advantages of these services, their adoption is limited and markets are yet to realize its full potential. To enable the energy transitions is fundamental to increase the energy system flexibility, enhancing cooperation among DSOs and with TSOs and easing participation of all energy-related actors through the validation and large-scale demonstrationof adapted and proven cross-sectoral services, interoperable data exchange platforms for smartgrids operation and the creation of required system architecture framework that will enable the creation of new business models providing additional value to meet consumers’ needs in compliance with a stable regulatory framework.
We are looking for a brilliant and motivated post-doc to carry out research and co-supervise PhD students. We offer a full-time contract of initially 36 months duration and an initial yearly gross salary of € 33.837 in a stimulating and warm environment.
The candidate will also benefit from opportunities to attend international academic conferences, enjoy research stays at prestigious international centres, and access to an extensive international research network, industry connections, and policy audience. The candidate will join strong author teams to publish papers aimed at high-ranked journals.
PhD degree in electrical engineering, Environmental Sciences, Computational/Analytics or Consumer behaviour. C1 English level. The candidate will pursue research on the social dimension of the energy transitions. In particular, s/he will design social strategies aimed at facilitating adoption of flexibility-based energy servcies and will measure the impact of different approaches on social, environmental and economic outcomes. The candidate will also contribute to defining more sustainable business models for these new energy services. Advanced user-level of statistical and machine learning techniques is necessary for this .
Curriculum vitae, academic record, cover letter and two recommendation letters.
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