24 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"Dr"-"FCiências" 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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the candidate will explore the limits of the different ex-situ and in-situ techniques to provide accurate information in comparison with data provided by the analysis of synchrotron radiation and STEM over
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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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project around the development of AI models for predicting promising catalyst candidates to integrate molecular modelling techniques, experimental data bases and materials data bases together with novel AI
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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
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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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(S)TEM. Requirements: Education: MSc in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science. Knowledge: Deep expertise in electron microscopy, particularly STEM and FIB methods
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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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computer-based systems and the preparation of data for inclusion in lab books, presentations and publications. Maintain a hardcopy or electronic lab book · Work in compliance with relevant Health and Safety
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: Education: MSc in Physics, Materials Science, Nanoscience, Computer Engineering, Data Science. Knowledge: Deep expertise in electron microscopy, particularly STEM and FIB methods. Proven experience in