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PhD Studentship: Intelligent Molecular & Process Design for Advanced CO2 Electrochemical Sorbents (IMPACT)

UCL - Process Systems Engineering

Qualification Type: PhD
Location: London
Funding for: UK Students, EU Students, International Students
Funding amount: circa £22,780 per annum + UK fees
Hours: Full Time
Placed On: 17th March 2026
Closes: 13th April 2026

Stipend: circa £22,780 per annum + UK fees

Duration of Studentship: 4 years

Start date: May 2026 (or as soon as possible thereafter)

Vacancy information

The Department of Chemical Engineering at the University College London (UCL) invites applications for a fully funded 4-year PhD program in Process Systems Engineering.

The project will focus on the development of an AI-enhanced design framework for novel electrochemical absorbents for CO2 capture.

Funding for this project covers the home tuition fees and a stipend.

Studentship description

How can we accelerate the discovery of new sorbents for electrochemical CO2 capture using artificial intelligence? Electrochemical CO2  capture is emerging as a highly promising alternative to conventional carbon capture technologies, with the potential to deliver more energy-efficient, flexible, and sustainable separation processes. However, their success depends critically on the discovery of high-performance sorbents that combine strong CO2  affinity with efficient electrochemical regeneration.

This PhD project will develop next-generation computational tools to accelerate the discovery of such materials. The successful candidate will work at the interface of chemical engineering, machine learning, molecular design, and sustainability, helping to create smarter ways of identifying promising sorbents for electrochemical CO2 capture.

Over the course of the project, the student will have the opportunity to: investigate the key scientific and engineering challenges involved in designing sorbents for electrochemical CO2 separation, including the analysis of existing datasets, modelling approaches, and performance metrics; develop physics-informed and data-efficient machine learning models to predict sorbent behaviour from sparse and multi-modal experimental data; and devleop inverse (or generative) design and screening tools to identify new candidate molecules within realistic chemical, electrochemical, and process constraints.

With strong connections to the Sargent Centre and industrial partners, you will receive interdisciplinary training in machine learning, molecular and process design, and process systems engineering, and have access to global experts and cutting-edge research facilities.

Person specification

We are seeking a highly motivated and talented student with, or expecting to obtain, at least a 2:1 degree at MEng, MSc, or equivalent level in Chemical Engineering, Chemistry, Physics, or a closely related discipline. Experience in machine learning, numerical optimisation, or process systems engineering would be advantageous. Familiarity with electrochemical CO2 capture, redox-active materials, or amine-based absorbents would also be beneficial, although candidates with strong potential and an interest in developing expertise in these areas are equally encouraged to apply.

Eligibility

Applications from overseas applicants should be able to demonstrate that they can cover the difference between home and overseas tuition fees.

How to apply

Applications should be submitted through:

https://evision.ucl.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RRDCENSING01&code2=0041

Funds are only available to cover UK-equivalent fees. Overseas students may apply, provided they can independently cover the difference between UK and overseas tuition fees.

Please nominate Dr Lauren Lee as supervisor and include a statement of interest.

For informal enquiries please contact Dr Lauren Lee at: lauren.lee@ucl.ac.uk

For further information on the MPhil/PhD course as well as the recruitment and selection process, please click on the link below:

https://www.ucl.ac.uk/chemical-engineering/study/mphilphd

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