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areas of nanoscience and nanotechnology. Job title: PhD Student in Computational Materials Science Research area or group: Theory and Simulation Group Description of Group/Project: The Theory and
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areas of nanoscience and nanotechnology. Job Title: Postdoctoral Researcher (Computational Materials Science - AI for Catalysis) Research area or group: Theory and Simulation Group The Australia-Spain
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PhD position funded by AEI agency (Spanish Government) TOPIC: Magneto-ionics for information technologies: secure and energy-efficient memories and advanced computing A PhD position is available
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ICN2 PhD Programme Programme description: The ICN2 offers a competitive PhD Programme to talented students who wish to develop a research career at one of the top European institutions in the field
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areas of nanoscience and nanotechnology. Job Title: Research Assistant - AI Optimisation for Materials Design Research area or group: Theoretical and Computational Nanoscience Group Description of Group
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Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
different areas of nanoscience and nanotechnology. Job Title: Research Engineer - Tools developer for LSQUANT platform Research area or group: Theoretical and Computational Nanoscience Group Description
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interfacing, thin-film technology and nanofabrication, growth and characterization techniques and neural data analysis. The research assistant will specialize in multiple facets of neural interfaces development
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. · Handle sensitive information and maintain confidentiality of all personal and corporate matters. · Engage with multiple stakeholders, internally and externally, using judgement and discretion
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to) SIESTA (www.siesta-project.org) and its TranSIESTA functionality. SIESTA is a multi-purpose first-principles method and program, based on Density Functional Theory, which can be used to describe the atomic
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Synchrotron developing correlative (S)TEM in-situ experiments on catalysts, from SACs to multiple types of nanostructures. This position is linked to the AMADE PID2023 project funded by AEI / MCIN, led by Prof