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equivariant GNNs (e.g., E(3)-equivariance), MACE or related message-passing models. Familiarity with force fields methods. Summary of conditions: Full time work (37,5h/week) Contract Length: 6 months Location
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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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based on catechol molecules, present in various living organisms (e.g., mussels), have demonstrated unprecedented adhesive properties under wet conditions, biocompatibility, low toxicity and low cost
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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