Research Fellow in AI and Computational Chemistry

University of Leeds • Full-time • Leeds, GB • 3w ago

Are you interested in developing interpretable AI models for the next generation of green syntheses? Do you have experience in AI/Machine Learning, or computational modelling of organic reactions? Do you want to work in a high interdisciplinary at the heart of one of the UK’s leading research-intensive universities?

The switch from traditional organic solvents, many of which are hazardous, volatile or non-sustainable, to modern green solvents is one of the key sustainability objectives in High Value Chemical Manufacture. Currently, the use of green solvents is often explored at process development stage, instead of discovery stage, leading to re-optimisation, longer development time, cost, and additional uncertainty. On the other hand, selecting the right solvent early may enhance chemoselectivity, avoid additional reaction steps, and simplify purification of the products.

Predicting these changes is an important underpinning capability for wider adaptation of green solvents in manufacturing, and there is an urgent need for ML models which predict reactivity in green solvents based on available data in traditional solvents. In this interdisciplinary project, you will develop solvent-dependent reactivity and reaction selectivity prediction models for green solvents, based on reactivity data curated from the literature and DFT/cheminformatics derived reactivity descriptors. You will also produce a standard set of substrates based on cheminformatics analysis of industrially relevant reactions for reaction scope, and limitations study by the synthetic community.

These outputs will have transformative impacts in the chemical manufacture industry, delivering rapid, more sustainable and better quality-controlled processes through shorter development time, and confidence in predicting reaction outcomes in green solvents. The project will be carried out with support from industrial partners working in the field of cheminformatics and AI/Machine learning and end-users in High Value Chemical Manufacturing: Lhasa Ltd., Molecule One, AstraZeneca, CatSci, and Concept Life Science.

You will work in a collaborative research team based in the Institute of Process Research & Development , and will lead the analysis of curated reaction data and will develop reactivity descriptors based on 2D and 3D structures (generated with high throughput DFT calculations) of organic substrates and reagents. You will develop a set of standard substrates based on analysis of industrial substrates and lead the development of solvent-dependent reactivity prediction models in green solvents. You will co-ordinate with collaborators at University of Southampton (data mining and curation) and Imperial College London (experimental data collection and validation) on these tasks and will manage collaborations with industrial partners during the project. You will employ High Performance Computing, Python programming, DFT calculations and ML algorithms to deliver the objectives of the project.

With a PhD in Chemistry (or have submitted your thesis before taking up the role), you will have a strong background in Python programming and computational chemistry and experience in working in an interdisciplinary team with industrial partners.

To explore the post further or for any queries you may have, please contact:

Dr Bao Nguyen , Associate Professor

Tel: +44 (0)113 343 0109 or email: B.Nguyen@leeds.ac.uk

Webpage: https://baonguyen.group

Location: Leeds - Main Campus

Faculty/Service: Faculty of Engineering & Physical Sciences

School/Institute: School of Chemistry

Category: Research

Grade: Grade 7

Salary: £37,099 to £44,263 p.a.

Due to funding restrictions, an appointment will not be made higher than £39,347 p.a.

Working Time: 37.5 hours per week

Post Type: Full Time

Contract Type: Fixed Term (Up to 4 years due to grant funding)

Release Date: Friday 25 August 2023

Closing Date: Sunday 24 September 2023

Interview Date: To be confirmed

Reference: EPSCH1089

Downloads: Candidate Brief