20 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Fundação-Minerva-" positions at ICN2
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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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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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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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. 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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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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-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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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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+ EDX · Fully Automated FIB Helios 5UX · FEI SEM Quanta and SEM Magellan Requirements: · Education: PhD in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science. · Knowledge: Deep
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Research Engineer - Tools developer for LSQUANT platform (Theoretical and Computational Nanoscience)
of Group/Project: The TCN group is launching an activity on marrying Artificial Intelligence with its activities and numerical tools to access charge transport information in complex (disordered) van der