Data-driven prediction of nanoparticle geometry in real-time

The project is placed on the interface of colloidal chemistry, computational plasmonics, and artificial intelligence. The student will develop an analytic tool for predicting size and shape of nanocrystals in a colloidal solution using an optical fingerprint as prior knowledge and a trained machine learning model. The outcome of the project will allow for accelerating the mechanistic studies on the growth of the nanocrystals. From a broader perspective, the project's outcome paves the way of transferring current technologies in computational plasmonics and artificial intelligence into innovation for improved characterisation of new nanomaterials of relevance in biosensing. We look for a candidate with a strong background in physics and excellent skills in Python programming language. The successful candidate will have the opportunity to work in an interdisciplinary environment.