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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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Mathjobs.org | 26 days 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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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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constitutive modeling of anisotropic and compressible materials. Minimum Qualifications Required PhD in mechanical engineering, aerospace engineering, materials science and engineering, civil engineering
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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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-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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ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real
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interfaces and defects. This research will combine electronic structure methods, artificial intelligence, and data-driven techniques to understand and engineer novel materials for next-generation
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, Applied Mathematics, or a related field. Strong expertise in: Computer vision and image processing Deep learning (e.g., PyTorch, TensorFlow) Data-driven modeling and analysis Proficiency in Python and