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Researchers in AI-driven atomistic modeling and AI-accelerated cheminformatics to join the Otaniemi Center for Atomic-scale Materials Modeling (OCAMM), hosted by the Department of Chemistry and Materials
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quantification Extend the modeling framework to porous and cellular materials, such as polymer and metal foams, including compressible constitutive formulations, calibration against experimental data, and
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-driven mechanical characterization of solid and porous materials, supported by an NSF CMMI CDS&E award and industry-sponsored projects. The primary function of the position is to develop and cross
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Associate in the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials
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Associate in the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials
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Associate in the Peng Research Group . The successful candidate will conduct research at the intersection of scientific AI, atomistic simulation, computational catalysis, and data-driven materials
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-informed or data-driven (AI/ML) manufacturing approaches Demonstrated ability to work effectively in collaborative research environments Does this position have supervisory responsibilities? No Preferred
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-informed or data-driven (AI/ML) manufacturing approaches Demonstrated ability to work effectively in collaborative research environments Does this position have supervisory responsibilities? No Preferred
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ETH Zurich, we aim to understand how materials deform, degrade, break, and ultimately fail. Our research is driven by curiosity about the physical mechanisms that underlie failure and by the ambition
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activities and data-driven predictive modeling for precision medicine. In addition to our core research questions and mission, the unit will also serve as a nexus between multiple branches and divisions and