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Process in the Interviews phase Project Title: Data-Driven Optimization of PtAg Hollow Nanocrystals Synthesis - Master's degree internship Group:Inorganic Nanoparticles Group, ICN2 Supervisor: Dr
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‑Lab Notebooks with structured templates, enabling FAIR‑by‑design data generation. This includes the creation of institutional AI‑ready catalogues and benchmarks, interoperability with PIDs and metadata
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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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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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-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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of materials and devices following the recently upgraded ISOS protocols and in-situ characterization. Process and analysis of data. Elaboration of periodic reports to keep track of the progress of the project
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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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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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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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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