Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living. Empa is a research institution of the ETH Domain

Develop multi-material models at the atomic and molecular level to enable us to optimize materials processes and designsUsing experimentally obtained data you will optimize the next generation of materials through experimental design including but not limited to 3D printing of polymers, sintering of ceramics, joining of advanced ceramicsYou will be expected to perform processing experiments and to measure and investigate key mechanical and microstructural propertiesPresent your results in scientific publications and at international conferencesSupervision of BSc and MSc students Your profile

• We are looking for a highly creative self-motivated team player
• You have a PhD degree in Materials Science or Chemistry combined with Machine Learning
• In depth experience of Python, VASP, PySCF, classical mechanics, scientific IT and data handling
• Experience in neural network architectures, bayesian optimization, random forest, kernel models, or gaussian process regression
• Worked with libraries such as pandas, github, tensorflow, pytorch, matplotlib and scikitlearn
• A proven track record in publishing peer reviewed articles
• Fluent in oral and written English