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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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build using molecular dynamics, the MACE foundation models and density functional theory. Main Tasks and responsibilities: AI4LSQUANT aims to accelerate quantum modelling by learning fast, accurate
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Materials group (NANOSFUN) is a research group of the Catalan Institute of Nanoscience and Nanotechnology (ICN2) that focuses on the research and development of novel molecular and polymeric functional
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Professional Experience: PhD and at least 3 years postdoc experience in relevant areas of expertise. Personal Competences: Diligent, enthusiastic, ability in theory and experimental physics. Summary
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conditions in a vibrant, multidisciplinary and international research environment. Within this project, the candidate will characterise well-defined electrocatalyst materials and investigate the molecular
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of research protocols, SOPs and related documentation · Be responsible for the recording, documentation and reporting of all preclinical models used by the Nanomedicine Lab · Perform cross-faculty collaborative
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optimiser that accelerates both workflow efficiency and materials discovery. Main Tasks and responsibilities: AI4LSQUANT aims to accelerate quantum modelling by learning fast, accurate surrogates
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familiarity with at least one of: DFT workflows, Wannier/TB model building, or quantum-transport simulations; willingness to become hands-on across the stack. Comfortable with Linux/HPC, job schedulers, and