Nanoscience and nanotechnology arose in the last decades at the frontline of a broad range of research fields, due to the highly challenging and unexpected properties of nanomaterials and nanosystems. These properties enable nanomaterials in various shapes (0D, 1D, 2D, 3D) and phases or nanosystems to be applied in a wide range of applications, from biomedical to industrial engineering. Furthermore, these materials frequently serve as an interdisciplinary bridge between diverse scientific and technical fields.
When dealing with the impacts of numerous processes occurring at the nanoscale, the application of machine learning (ML) techniques has become more attractive given the rising complexity of the data while enabling remarkable advancements that push forwards the broad spectrum of nanostructured materials-based technology. Furthermore, it is evident that, for such materials, theoretical formulations and ML-based solvers dealing with mesoscopic or macroscopic descriptions are required tools to improve experiments and practical investigations.
Nanomaterials | Fabrication technologies | Electromaterials | Machine learning | Computer science
You will work in the state of art laboratories at the Graduate School of Biomedical Engineering and will be supervised by a multidisciplinary team of materials engineer and computational scientist at Dr Esrafilzadeh and Dr Rokny's research laboratories.
This project aims to design a model for the synthesis and fabrication of different electromaterials on the nanoscale using ML techniques. The knowledge gained through ML techniques will assist in selecting the optimum conditions to tune the materials with higher conductivity, electron mobility, and flexibility to be integrated into future additive fabrication processes.