Are you fascinated by the emergence of intelligence in materials? Join our interdisciplinary research team at Utrecht University to explore how simple building blocks can exhibit learning behavior. This fully-funded PhD position offers a unique opportunity to work at the forefront of physics, materials science, and computation.
Your job The Emergence at all Scales (EAAS) consortium is the national flagship of the
Dutch Institute for Emergent Phenomena (D-IEP), a game-changer in the NWA Route 2 initiative. EAAS unites eight Dutch universities, one Ukrainian university, and Statistics Netherlands (CBS) in an interdisciplinary effort to understand emergent phenomena across scales. Our research combines multiple fields including physics, mathematics, astronomy, history & philosophy of science, and social science. Its approach to societal engagement throughout the project’s five-year lifetime is equally interdisciplinary, with a wide variety of activities ranging from art/science programmes, large scale science festivals, citizen science and educational initiatives at various levels.
EAAS is hiring 20 PhD/postdoc researchers across various fields, and we are committed to achieving gender parity in our recruitment. We now invite applications for a PhD position in the sub-project "Intelligent Matter", led by Professor Marjolein Dijkstra in the
Soft Condensed Matter (SCM) group at the
Debye Institute for Nanomaterials Science, Utrecht University. The position is offered in collaboration with Peter Bolhuis (UvA/chemistry), René van Roij (UU/physics), Martin van Hecke (AMOLF/UL/physics), Corentin Coulais (UvA/physics), Senja Barthel (VU/mathematics).
This project aims to explore the emergence of intelligent behaviour in materials composed of simple building blocks, such as water, ions, molecular receptors, lipids, and colloids. Using theory and simulations, we seek to understand how collective interactions lead to self-organised learning behaviours. From understanding the mechanisms for “learning”, leveraging topological defects in liquid crystals for information processing, to the hydrodynamic description of water and ion transport through neuromorphic networks, we aim to design materials with learning capabilities. Our approach involves exploring multistability, hysteresis, and non-linearity in soft materials for memory, learning, and computation. By systematically designing simplified models and conditions, we aim to understand and control emergent dynamic properties, and to gain insight into the emergence of cognitive functions and intelligent behaviour across various systems. This will enable us to create learning behaviour in cutting-edge materials. This pursuit may lead to smarter materials capable of storing and processing information, and even performing computations.