18 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr"-"P" 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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. 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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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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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
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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
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of stability protocols for memristor characterizaiton. · Process and analysis of data. · Elaboration of periodic reports to keep track of the project progress. · Preparation of scientific manuscripts and
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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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data storing and analysis (including AI). Requirements: A Bachelor and Master Degree in Nanoscience and Nanotechnology, Biotechnology, Chemistry, Materials Science or similar. Knowledge and Professional
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. Applicants are invited to propose a research project around the development of AI models for predicting promising catalyst candidates to integrate molecular modelling techniques, experimental data bases and