"Energy efficient building control can have a major impact on reducing the overall energy consumption since a large part of today's energy is consumedin buildings. The use of model predictive control (MPC) together with weather and occupancy predictions is a promising approach to realize significantenergy savings.The control of power grids is of major concern due to the increasing number of renewable energy sources which introduce additional variability. This can bemitigated against by making use of additional storage, e.g. by leveraging the thermal energy storage of buildings.These areas demand for advanced and novel control solutions, which can handle uncertainty, large-scale systems, distributed systems, and predictions of the future system behavior. The aim is to use recent advances in random convex programming, couple these to MPC and develop a novel stochastic MPC formulation that is tractable for large-scale systems.The key objectives of this project are:- Development of stochastic MPC strategies, which address the above challenges.- Development, implementation and testing of a new energy efficient control strategy for buildings on a test-bed at UC Berkeley.- Formulation of tractable dynamic power flow problems involving a stochastic formulation and including buildings as additional storage in the formulation.The project is designed as a structured two-year program, where the first year is carried out at the University of California, Berkeley under supervision of Prof. Tomlin and the second year is carried out at the Swiss Federal Institute of Technology Zurich under supervision of Prof. Morari.The project will provide the fellow with excellent opportunities to further develop her competences and skills. Furthermore, it will strengthen the collaboration and establish a research network between the two hosts and contribute to Europe's excellence and competitiveness."