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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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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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Computational and Method Development Contribute to development and testing of algorithms for EAD spectral interpretation and annotation Integrate metabolomics data with statistical modeling and
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international team combines materials chemistry, optical spectroscopy, electron microscopy, theoretical modeling, and numerical simulation to understand and control materials at the nanoscale. Project background
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modeling, device fabrication and machine learning and intelligent system design. Candidates must have a Ph.D. in materials science and engineering, electrical engineering, chemical engineering, chemistry
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materials characterization Strong background in computational materials science and machine learning applications Proficiency in programming and data analysis languages (LabVIEW, Python, MatLab, OriginPro
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requirements prior to employment start date. Additional Required Qualifications Ph.D. in Physics, Chemistry, Materials Science, Chemical or Electrical Engineering, or a related field. Experience in the growth
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-balance, and process-performance data needed to support reactor design, process modeling, techno-economic analysis, and transition from laboratory systems to pilot-scale operation. Establishing CMAs and