PhD Position in Multiscale Modeling of Smart Self-Sealing Materials

This PhD position at the University of Amsterdam focuses on advancing multiscale computational modeling to design smart, self-sealing materials combining metal-organic frameworks (MOFs) and peptides. The candidate will develop coarse-grained and atomistic simulations, surrogate models, and machine learning techniques to predict and optimize the adaptive properties of these materials, such as breathability and self-assembly. The work involves close collaboration with an experimental PhD colleague and external partners, including TNO, to validate computational predictions through experiments. The project aims to derive design rules for guiding experimental testing and improving material performance, contributing to both fundamental science and applied technology.

• Develop coarse-grained and atomistic simulations to study peptide-MOF interactions and aggregation mechanisms at material surfaces.
• Construct highly coarse-grained models of MOFs with tunable pore and surface chemistry to analyze physicochemical properties of peptide-loaded materials.
• Build surrogate models and apply machine learning techniques to derive design rules and rapidly screen thousands of MOF-peptide combinations.
• Collaborate closely with an experimental PhD colleague to integrate computational and experimental results.
• Validate computational predictions through experiments at TNO and contribute to the project’s international scientific dissemination.
• Publish research findings, present at conferences, and co-supervise undergraduate and master’s students.
• Participate in teaching activities for undergraduate and master’s students within the department.