Research Fellow in AI and Computational Chemistry

University of Leeds

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Are you curious about creating interpretable AI fashions for the following era of inexperienced syntheses? Do you have got expertise in AI/Machine Studying, or computational modelling of natural reactions? Do you wish to work in a excessive interdisciplinary on the coronary heart of one of many UK’s main research-intensive universities?

The change from conventional natural solvents, a lot of that are hazardous, unstable or non-sustainable, to fashionable inexperienced solvents is likely one of the key sustainability targets in Excessive Worth Chemical Manufacture. At present, the usage of inexperienced solvents is commonly explored at course of improvement stage, as an alternative of discovery stage, resulting in re-optimisation, longer improvement time, value, and extra uncertainty. Then again, choosing the suitable solvent early could improve chemoselectivity, keep away from extra response steps, and simplify purification of the merchandise.

Predicting these adjustments is a vital underpinning functionality for wider adaptation of inexperienced solvents in manufacturing, and there may be an pressing want for ML fashions which predict reactivity in inexperienced solvents based mostly on accessible knowledge in conventional solvents. On this interdisciplinary venture, you’ll develop solvent-dependent reactivity and response selectivity prediction fashions for inexperienced solvents, based mostly on reactivity knowledge curated from the literature and DFT/cheminformatics derived reactivity descriptors. Additionally, you will produce a normal set of substrates based mostly on cheminformatics evaluation of industrially related reactions for response scope, and limitations examine by the artificial neighborhood.

These outputs could have transformative impacts within the chemical manufacture business, delivering fast, extra sustainable and higher quality-controlled processes via shorter improvement time, and confidence in predicting response outcomes in inexperienced solvents. The venture shall be carried out with help from industrial companions working within the area of cheminformatics and AI/Machine studying and end-users in Excessive Worth Chemical Manufacturing: Lhasa Ltd., Molecule One, AstraZeneca, CatSci, and Idea Life Science.

Working in a collaborative analysis workforce based mostly within the Institute of Course of Analysis & Improvement , you’ll lead the evaluation of curated response knowledge and can develop reactivity descriptors based mostly on 2D and 3D constructions (generated with excessive throughput DFT calculations) of natural substrates and reagents. You’ll develop a set of ordinary substrates based mostly on evaluation of commercial substrates and lead the event of solvent-dependent reactivity prediction fashions in inexperienced solvents. Co-ordinating with collaborators at College of Southampton (knowledge mining and curation) and Imperial Faculty London (experimental knowledge assortment and validation) on these duties; you’ll handle collaborations with industrial companions through the venture and make use of Excessive Efficiency Computing, Python programming, DFT calculations and ML algorithms to ship the targets of the venture. 

Holding a PhD in Chemistry (or have submitted your thesis earlier than taking over the function); you’ll have a robust background in Python programming and computational chemistry coupled with expertise in working in an interdisciplinary workforce with industrial companions.

To discover the submit additional or for any queries you will have, please contact: 

Dr Bao Nguyen , Affiliate Professor

Tel: +44 (0)113 343 0109 or electronic mail: [email protected]

Location:  Leeds – Fundamental Campus
School/Service:  School of Engineering & Bodily Sciences
College/Institute:  College of Chemistry
Class:  Analysis
Grade 7

£37,099 to £44,263
Attributable to funding restrictions, an appointment is not going to be made increased than £39,347 p.a.
Working Time:  37.5 hourts per week
Put up Sort: 

Full Time
Contract Sort:  Mounted Time period (As much as 4 years – To finish particular time restricted work)
Launch Date:  Friday 17 November 2023
Closing Date:  Monday 01 January 2024
Interview Date: 

To be confirmed
Reference:  EPSCH1095
Downloads:  Candidate Transient

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