AI approaches for NMR analysis of biomolecular mixtures

Description

This postion will remain open until a suitable candidate has been found.

This is an interdisciplinary project (Computer Science and Biochemistry) aimed at the development of novel AI methods for the analysis of Nuclear Magnetic Resonance spectra.

Renewable biomolecules are replacing traditional chemicals to reduce pollution, energy waste and carbon footprint. Nuclear Magnetic Resonance (NMR) provides detailed information on these materials, but the data sets are very complex, making automated  computer assisted analysis  challenging. The project  aims to   develop novel AI   approach to NMR spectra analysis, based on automated reasoning and machine learning. We propose (1) to explore applications of Constraint Satisfaction/SAT solving methods to the reconstruction of molecular structures from a combination of NMR spectra, (2) to explore supervised machine learning (ML_ approaches, including, but not limited to, relational learning and generative adversary networks, for the recognition of molecular signatures in the spectra of complex mixtures.

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