| KEYWORD |
Machine learning approaches for the study of complex molecular systems
keywords COARSE-GRAINING, COMPLEX SYSTEMS, MACHINE LEARNING, MOLECULAR DYNAMICS, MOLECULAR MODELING AND SIMULATION, MULTISCALE SIMULATION, PATTERN RECOGNITION, PHYSICAL CHEMISTRY, SELF-ASSEMBLY, STATISTICAL PHYSICS, SUPRAMOLECULAR CHEMISTRY
Reference persons GIOVANNI MARIA PAVAN
Research Groups COMPUTATIONAL PHYSICAL CHEMISTRY (CPC) LABORATORY
Thesis type COMPUTATIONAL, MODELING AND SIMULATION
Description Nature uses self-assembly to build fascinating supramolecular materials, such as microtubules and protein filaments, which can self-heal, reconfigure, adapt or dynamically respond to specific stimuli. In these systems, the constitutive building blocks are in continuous exchange and communication with each other and with the external environment, in what de facto constitutes a complex molecular system. The determinants of the dynamic behavior of such systems, and the sources of their emergent properties are typically difficult to investigate. At the same time, achieving this goal would open the way toward new concepts in the design of artificial molecular systems with bioinspired properties.
This thesis is related to the ERC Consolidator grant project "DYNAPOL," which aims at understanding how to design new types of molecular systems with bioinspired dynamic properties by combining multiscale molecular models, advanced computational simulation, and machine learning approaches.
See also https://www.gmpavanlab.com/
Notes During the thesis, the student will have the opportunity to work in close contact with worldwide renowned researchers and scientists, national and international academic and industrial partners.
Deadline 07/09/2024
PROPONI LA TUA CANDIDATURA