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of Group/Project: We are building an optimisation-driven framework that makes AI models reliably operate advanced scientific software (e.g., DFT, Wannierisation, and quantum-transport codes) and (ii) uses
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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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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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/scalable processes. These excellent features turn bioinspired catechol-based materials unique for their use in coatings applications. Our society must change the water usage model, ensuring its purity
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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