Tips on how to manage your PhD stipend FIND OUT MORE
Coventry University Featured PhD Programmes
University of West London Featured PhD Programmes
University of Reading Featured PhD Programmes

Machine Learning Model for Amorphous CO2 Reduction Electrocatalysts

School of Biological & Chemical Sciences

London United Kingdom Computational Chemistry Materials Science Physical Chemistry Statistics

About the Project

The following PhD studentship is available in the School of Biological and Chemical Sciences to start in September 2021.

Research Environment

The School of Biological and Chemical Sciences at Queen Mary University of London (QMUL) is one of the UK’s elite research centres, according to the 2014 Research Excellence Framework (REF). We offer a multi-disciplinary research environment and have approximately 160 PhD students working on projects in the biological, chemical and psychological sciences. Our students have access to a variety of research facilities supported by experienced staff, as well as a range of student support services.

The labs led by Crespo-Otero ( and Di Tommaso ( are two active Computational Chemistry research groups based in the Department of Chemistry at QMUL. We are executive members of the London’s Thomas Young Centre (TYC) for the theory and simulation of materials ( Our research focuses on the development and application of computational & theoretical tools to solve challenging chemical problems. Our groups are members of the interdisciplinary Materials Research Institute that brings together a wide range of expertise from Queen Mary based academics with interest in materials research.

Greg Slabaugh is professor in Computer Science at QMUL. His group develops machine learning algorithms to analyse complex datasets using techniques such as discriminative and generative deep neural networks as well as random forest methods. Slabaugh is Director of the newly formed Digital Environment Research Institute, which brings together world leading researchers from across QMUL faculties to drive new multidisciplinary research in data science and applications.

Training and Development

Our PhD students become part of Queen Mary’s Doctoral College, which provides training and development opportunities, advice on funding, and financial research support. Our students also have access to a Researcher Development Programme designed to help recognise and develop the skills and attributes needed to manage research and to prepare and plan for the next stages of their career.

The PhD student on the project will work between the Theoretical & Computational Chemistry Lab housed in the Department of Chemistry and the Digital Environment Research Institute in the Department of Computer Science. The student will be equipped with a high-performance mobile workstation and given access to outstanding institutional and national supercomputing facilities. As part of the Thomas Young Centre, the student will have unprecedented opportunities to follow postgraduate courses in materials & molecular modelling, and opportunities for collaboration, networking & training in molecular modelling (Nature Materials, 2016).Through our membership to the Alan Turing National Institute for data science and artificial intelligence and Archer, the UK Supercomputing Service, the student will have access to scientific software development training. 

Project Details

The newly formed “Centre for CO2 Conversion” at QMUL is seeking PhD applicants for their fully funded mini Centre for Doctoral Training to start in September 2021. The rising level of CO2 in Earth’s atmosphere is the single most important environmental challenge that our society must face. One of the most attractive solutions to reduce climate change emissions is the chemical conversion of CO2 into added-value chemicals (carbon monoxide, formic acid, ethanol, and others). The main challenge lies in the inertness of CO2, making catalysts requisite to decrease the energy required to convert CO2 to hydrocarbons.

AmorCO2RR is an academic-industrial collaboration between eight leading Queen Mary academics with external partners from industry (Johnson Matthey, RISE), governmental organizations (National Physics Laboratory) and other leading academic institutions (UCL, National University of Singapore). The objective of AmorCO2RR is to develop a novel class of amorphous catalysts for the photo-electrochemical CO2 conversion.

The successful applicant for this PhD student will develop machine-learning protocols to design and predict the activity of catalysts for CO2 reduction, thereby expediting the discovery with lesser cost as compared to traditional empirical methods. 

Experience Required and Application Process

Applications are invited from outstanding candidates with or expecting to receive a first or upper-second class honours degree and a masters degree in an area relevant to the project: Physics, Computer Science, Computational Chemistry, or Materials Science. The successful applicant will demonstrate strong interest and self-motivation in scientific software development, and the ability to think analytically and creatively. A strong interest in multidisciplinary projects are essential.

Applicants who are not nationals of a majority English speaking country are required to provide evidence of their English language ability. Please see our English language requirements for details.

Formal applications must be submitted online by the stated deadline including your CV, certificates and transcripts for previous degrees, a personal statement and 2 references. Please see further details on the application process and find the link for the online application form on our website.

The School of Biological and Chemical Sciences is committed to promoting diversity in science; we have been awarded an Athena Swan Silver Award. We positively welcome applications from underrepresented groups.

Funding Notes

This studentship is open to UK residents eligible for 'home' fee status and is funded by Queen Mary University of London. It will cover tuition fees, and provide an annual tax-free maintenance allowance for 3 years at the Research Council rate (£17,285 in 2020/21).

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here

The information you submit to Queen Mary University of London will only be used by them or their data partners to deal with your enquiry, according to their privacy notice. For more information on how we use and store your data, please read our privacy statement.

* required field

Your enquiry has been emailed successfully

Search Suggestions

Search Suggestions

Based on your current searches we recommend the following search filters.

FindAPhD. Copyright 2005-2021
All rights reserved.