Postdoctoral position in entropy enhanced defects for sustainable catalysis
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
The postdoctoral researcher will work on quantum chemical (QC) modeling of amorphous materials and undercoordinated defects on oxidic surfaces. The postdoc will work in a team of scientists with diverse backgrounds and will tightly interact with experimentalists. In this project, by comparing modeling results and experiments, we aim to establish a framework to digitally accelerate the design of sustainable amorphous catalysts by integrating computational and analytical methods. Your profile
The applicants must hold a recent PhD degree, preferably in physics, chemical engineering, materials science, or mechanical engineering. Knowledge of the theory and demonstrated proficiency with QC modeling is mandatory for this position: the candidate must have an excellent record of accomplishments and publications (for the corresponding career stage) that proves an extensive hands-on experience and the ability to autonomously work with QC codes (e.g., CP2K, Quantum Espresso, or VASP). Knowledge of machine learning algorithms and experience in dealing with real data are an asset. The candidate is a curious, passionate, and proactive individual that can autonomously advance the project, engaging in discussion with the other members of the research team.
The postdoctoral researcher will work on quantum chemical (QC) modeling of amorphous materials and undercoordinated defects on oxidic surfaces. The postdoc will work in a team of scientists with diverse backgrounds and will tightly interact with experimentalists. In this project, by comparing modeling results and experiments, we aim to establish a framework to digitally accelerate the design of sustainable amorphous catalysts by integrating computational and analytical methods. Your profile
The applicants must hold a recent PhD degree, preferably in physics, chemical engineering, materials science, or mechanical engineering. Knowledge of the theory and demonstrated proficiency with QC modeling is mandatory for this position: the candidate must have an excellent record of accomplishments and publications (for the corresponding career stage) that proves an extensive hands-on experience and the ability to autonomously work with QC codes (e.g., CP2K, Quantum Espresso, or VASP). Knowledge of machine learning algorithms and experience in dealing with real data are an asset. The candidate is a curious, passionate, and proactive individual that can autonomously advance the project, engaging in discussion with the other members of the research team.
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