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Field
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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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Mathjobs.org | about 1 month ago
discovery: AI-assisted simulation, surrogate modeling, inverse learning problems, automated scientific discovery, generative approaches for science, and data-driven discovery of mathematical or physical
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discovery: AI-assisted simulation, surrogate modeling, inverse learning problems, automated scientific discovery, generative approaches for science, and data-driven discovery of mathematical or physical
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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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modeling, multimodal data fusion, interpretable machine learning, NLP, text mining, or automated extraction of materials data from the literature Experience with workflow automation, data infrastructure
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related in vitro disease-modeling experiments. Design and execute hypothesis-driven studies related to epithelial cell fate specification, airway/lung epithelial maturation, hillock-like epithelial cell