PhD position: scale effects in highly-renewable energy systems

Updated: 24 days ago
Job Type: Permanent

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100%, Zurich, fixed-term

The Chair of Energy Systems Analysis (ESA), within the Department of Mechanical and Process Engineering at ETH Zurich, is looking for a doctoral student working on geospatial methods for resource assessments of renewable energy technologies. The PhD position is well connected with other research projects at ESA, which is at the intersection of engineering and economics, and involves developing and applying energy system models to diverse energy systems and research questions. The ESA group is headed by Prof. Dr. Russell McKenna and generally focuses on the energy system analysis of low carbon energy systems and technologies. The Chair holder’s joint appointment as the Head of the Laboratory of Energy Systems Analysis at PSI mean a strong collaboration with this group is also desirable.


Project background

The transition to a net-zero-carbon-emissions (net-zero) energy system requires a significant increase in renewable generation capacity with respect to current values. However, where exactly all this infrastructure should be built and at what scale, as well as the integration with existing infrastructure, raises many questions. This calls for a holistic analysis of future energy systems within the energy trilemma paradigm, where economic, environmental, and energy security performance must be balanced.


Job description

The goal of the project is to develop quantitative methods and tools to analysis scale effects within highly-renewable energy systems, for example the question of optimum scales for autonomous decentralised energy systems. This will be done:

  • by adopting state-of-the-art modeling techniques to simulate the future spatial and temporal evolutions of highly-renewable energy systems;
  • by expanding and developing cutting-edge energy models to determine the optimal system evolution with respect to economic, environmental and supply security indicators;
  • by employing empirical data relating to energy network infrastructure in order to understand cost structures;
  • by classifying and clustering supply and demand configurations based on statistical information reduction techniques.

To address these points, you will have access to state-of-the-art computational capabilities, and you will be in contact with experts within ETH Zurich and worldwide.


Your profile

We are looking for a proactive and highly motivated candidate, with a MSc in a quantitative discipline (such as engineering, applied physics, geosciences or mathematics) from an internationally recognized University and an excellent academic track record. Ideally, you have a background in quantitative modelling and programming (e.g. Matlab, Python, Julia, GAMS, GIS). Knowledge of mixed method approaches, mixed-integer linear programming solvers (e.g. CPLEX, Gurobi) and/or machine learning techniques are a benefit.

You should be able to speak and write clearly in English. Furthermore, you will need to enjoy working in a competitive, dynamic, diverse, and international environment with other doctoral students and postdocs.


ETH Zurich

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.

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