PhD position in Chemical Systems Engineering (100%) in Zürich

ETH Zurich is one of the world’s leading universities specialising in science and technology. It is renowned for its excellent education, its cutting-edge fundamental research and its efforts to put new knowledge and innovations directly into practice. The Institute for Chemical and Bioengineering (ICB) at the Department of Chemistry and Applied Biosciences is seeking to fill a

Project motivation & description: The need to transition towards a more sustainable industry calls for advanced multi-objective decision-support tools embracing the three sustainability dimensions (i.e. economic, environmental and social). In this PhD project, we will develop process systems engineering concepts & tools to assist in the design and planning of more sustainable chemical processes, with emphasis on reducing water & energy consumption as well as waste and emissions in the production of a wide range of chemicals. Focusing on the combined use of process simulation, multi-objective global optimization and life cycle assessment, we will screen thousands of chemical technologies using state-of-the-art computational tools; this approach will be applied to major conventional chemical processes and emerging alternatives, including carbon capture and utilization, biomass & waste conversion, and polymers revalorization. The PhD project will, therefore, advance a more fundamental understanding of how to embed sustainability principles in the chemical industry, while developing tailored computational tools to assess and optimize a wide range of interconnected chemical routes.

The PhD student will have access to and acquire expertise on state-of-the art modelling & optimization tools, including process simulation (e.g. Aspen Plus, Aspen-HYSYS and gPROMS); life cycle assessment and related software packages (e.g. SimaPro, Ecoinvent); algebraic modelling systems (i.e. GAMS, Matlab), optimization solvers (e.g. CPLEX, DICOPT, SBB) and machine learning algorithms (e.g. artificial neural networks, support vector machine, etc.). The PhD project is embedded into a larger, interdisciplinary research effort with international collaborations.