The discovery of new materials is crucial to meeting many current societal challenges. One of the pillars of this discovery capacity is to have means of characterizing these materials which are rapid, reliable and whose measurement uncertainties are qualified, even quantified.

This PhD project is part of this approach and aims to significantly improve the different ion beam induced spectrometry (IBA) techniques using advanced artificial intelligence (AI) methods. This project aims to develop explainable, sober and reliable AI models for materials analysis.
The PhD project proposed here has three main objectives:

- Develop an uncertainty model using probabilistic machine learning techniques in order to quantify the uncertainties associated with a prediction.
- Due to the very large number of possible combinatory-generated configurations, it is important to understand the intrinsic dimensionality of the problem. We wish to implement means of massive dimensionality reduction, in particular non-linear methods such as autoencoders, as well as PIML (Physics Informed Machine Learning) concepts.
- Evaluate the possibility of generalization of this methodology to other spectroscopic techniques.