In this project you will help to understand the most cost-effective pathways to reduce greenhouse gas emissions in the energy system. You will work on network and energy system models that seek to maintain sufficient model detail and interdependencies, while not losing sight of the broader characteristics that determine efficient investment strategies. You will develop new methods and algorithms to tackle the extreme computational complexity of optimising models that combine the international scope of European energy markets, the local detail of network bottlenecks and the couplings between multiple energy sectors. Your tasks will include research on new algorithms to optimise network expansion, incorporate non-linearities in energy system models and account for uncertainty and imperfect market design. You will publish your results in international journals and conference proceedings, and also partake in the supervision of students. The existing modelling tool Python for Power System Analysis (PyPSA) will be further developed to support the modelling aims of the project. To ensure full transparency, all software, data and publications generated in the project will be made publicly available under open licences. This position is only suitable for part-time work under particular conditions.
A doctoral degree in computer science, mathematics, physics, engineering, economics or related numerate disciplines.Excellent analytical skills and experience in theoretical and applied modelling.Experience of power flow modelling and optimisation.Knowledge of energy system engineering and/or economics.Programming experience (any language, but the project will mostly be in Python and Julia).Commitment to full transparency in research methods, data and publications.
Please apply with a complete application folder (CV, cover letter, certificates) with at least 2 references and a current publication list.
Salary category 13, depending on the fulfillment of professional and personal requirements.
limited up to 3 years.
Application up to