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beyond conventional binary "bits" toward multi-level and analog memory systems that enable richer information encoding. A major thrust of the group is the development of in-memory and physical computing
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maintenance of the FAIR Knowledge Base, ensuring all experimental and simulation data is searchable and reusable. Professional-grade Python skills with a focus on maintenance. You will be responsible
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performance. A key aspect of the PhD will be the systematic documentation and curation of experimental data in a FAIR-compliant materials database, including synthesis protocols, characterisation results, and
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the development and assessment of neurotechnology aimed for invasive brain-computer interfaces. In particular, the work will include in vitro assessment of the performance of electrophysiology neural
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publication repository in iMarina, including advanced curation of data. Uploading the information in the DDD (UAB open access repository) Enhancement of documents with specific metadata, defined by
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implement Python scripts for the automated control of electron microscopes; design and execute automated experiments, including real-time feedback loops between acquisition and data analysis; maintain
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failure emerges at interfaces rather than just within materials combining hands-on experiments with rigorous data analysis translating test outcomes into better design, process, bonding, and packaging
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-corruption guide, supporting the curation of the ICN2 Ethics Code, coordinating and managing the signing of documentation, collaborating in obtaining data and preparing reports and providing the Generalitat de
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. Generate structural and electronic descriptors to support the development and training of machine learning models for materials discovery. Contribute to the definition of FAIR data standards for the results
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in altermagnets to encode and process information. The project involves close collaboration with experimental and theoretical partners, including large-scale synchrotron facilities. The work will be