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and oral communication skills in English Motivation for working in the laboratory, carrying out experiments, and data analysis Experience in one of the following fields is required from the chosen
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or similar. Running social simulations with AI agents using platforms like Concordia or similar. Coding in Python or a similar language for simulation, visualization, and advanced statistical analysis (causal
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manufacturing (printing, deposition) Working experience in clean room is highly valued Both hands-on and data analysis skills in classical materials characterization methods (i.e., TEM, SEM, XRD, XPS, NMR, IR
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, deposition) Working experience in clean room is highly valued Both hands-on and data analysis skills in classical materials characterization methods (i.e., TEM, SEM, XRD, XPS, NMR, IR, Raman, XAS, SAXS, etc
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such as breakwaters by using computational tools developed in our research group. These tools are based on discrete element method and they have bee validated against experimental data. You will start your
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community. The research will be carried out at the Department of Information and Communications Engineering, DICE, at Aalto University, Finland. The project environment offers excellent infrastructure
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. The Department of Architecture offers degree programs in architecture, landscape architecture, and interior architecture. For more information, please see http://architecture.aalto.fi/en/ . We are now looking for
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nanoporous media; Atomistic simulation of chemical reactions and/or thin-film growth; Data-driven cheminformatics for molecular modeling and/or design; Molecular dynamics simulations. Your background and
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necessary. You do not need to be yet an expert in distributed graph algorithms and quantum information theory, as long as you are familiar with the relevant areas of mathematics and are willing and able
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. The research will be carried out at the Department of Information and Communications Engineering, DICE, at Aalto University, Finland. The project environment offers excellent infrastructure for deep learning