# Statistical learning method for chemistry applications

Motivated by real problems in computational chemistry, in which the huge complexity of the chemical space may prevent the complete exploration of the space itself, this project aims at developing statistical learning tools to support chemists (but not only) in their research.

In particular, this project has two main objectives:

- Refine existing approaches, such as boosting algorithms or neural networks, to include domain-specific knowledge, in order to improve their performances;
- Develop measures of uncertainty around their results, to allow the users estimating how much they should trust the results.

The latter, in particular, is a topic of extreme interest in all the fields in which statistical/machine learning is implemented, and goes in the direction of explainable AI.

This project is a collaboration between the Department of Mathematics and the Hylleraas Centre for Quantum Molecular Sciences, with the external participation of the University of Bonn (Germany).

## Requirements

- Master’s degree in statistics or a related quantitative subject with proven competence in statistics.
- Candidates with documented experience in scientific programming will be prioritized.

## Supervisors

Associate Professor Riccardo De Bin

## Call 1: Project start autumn 2021

This project is in call 1, starting autumn 2021. Read about how to apply