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Artificial Intelligence, Quantitative Ecology, Computational Biology, or Microbial Ecology and Evolution to support research and educational programs in Environmental Microbiome/Genomics/Ecology, at
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, computational science, and information engineering focusing on the analysis and design of chemical systems to enhance the efficiency of the entire innovation cycle—from the creation of materials
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, photonics, nonlinear dynamics, applied mathematics, and computational science. Semiconductor-laser experience is not required. The successful candidate will work closely with experimental researchers, and
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science is desired, with Physics and/or Chemistry specialization as is strong data handling and programming skills, and experience of using computational tools for materials modelling. About the
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well suited for candidates with a background in computational materials science and engineering or related fields, who wish to deepen their expertise in physics‑informed machine learning for materials
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candidates with a Master's degree in (computational) chemistry, physics, or materials science who are curious about applied machine learning in the natural sciences. Prior machine learning or Python
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, Integrated Computational Materials Engineering (ICME) based methodology to solve complex material problems and to invent totally new materials with unprecedented properties. VTT ProperTune® combined with
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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational
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and technology policy, public policy, computational social science, or a related discipline. Preferred Qualifications: Researchers in innovation and entrepreneurship, economics of innovation
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Postdoctoral Associate in Responsible AI and Computational Social Science. Our research examines how artificial intelligence, online platforms, and the Web shape individuals, societies, and access to information