PhD Studentship: Autonomous AI-based simulations for advanced materials

at UCL
Published April 19, 2023
Location London, United Kingdom
Category Machine Learning  
Job Type Scholarship  


PhD studentship: Autonomous AI-based simulations for advanced materials

Application deadline: Monday, May 1, 2023

Vacancy information

University College London (UCL) is one of the world's leading universities, renowned for its research excellence and commitment to innovation and discovery. The Department of Chemical Engineering at UCL is a leading centre for chemical engineering research, home to a vibrant community working at the forefront of chemical engineering research and covering a broad range of scales from the molecular to the complex systems level.

You will work in the Bloomsbury campus in the heart of London, collaborating with leading experts in computer simulations seeking solutions to Grand Challenges (such as energy, reducing carbon dioxide emissions, materials, sustainable manufacturing, health and environment) based on significant advances in fundamental knowledge.

The post is fully funded for 3.5 years, starting September 2023 or shortly thereafter.

Studentship description

Accurate atomistic modelling is a crucial element of the complex research landscape that underpins the development of a green economy, allowing for designing and optimising energy-efficient, less polluting materials and processes by advancing our understanding of materials at the microscopic level. Artificial intelligence (AI) has proven to be a game-changer in the field, enabling the investigation of materials with fast, accurate and insightful new approaches.

We seek a highly motivated and talented candidate who wants to join our team and work on cutting-edge research to bridge the gap between accurate but computationally expensive quantum mechanical simulations and more extensive simulations based on empirical potentials. The research aims to develop an autonomous system for neural-network-based potentials that reproduces potential energy surfaces from quantum mechanical calculations. Neural networks will be the engines that drive atomistic simulations at unprecedented time and length scales. This will ultimately lead to the characterisation of thermochemical and thermophysical properties of liquids and solids, investigation of transport in nanostructures, and catalytic reaction mechanisms.

Person specification

The successful candidate will have completed or be close to completing a first-class degree at the MEng or MSc level in Chemical Engineering, Chemistry, Material Science, Physics or a related discipline. The candidate should be motivated to conduct cutting-edge research in AI applied to atomistic simulations of the condensed state. Experience in one or more of the following fields is welcome: density functional theory, molecular dynamics, statistical mechanics, or machine learning. Experience in scientific coding is desirable but not necessary.


A first-class degree at the MEng or MSc level is required.

Funds are only available to cover UK-equivalent fees.

Applications should be submitted through:

Please nominate Dr Marcello Sega as supervisor and include a statement of interest.

For informal enquiries please contact Dr Marcello Sega at

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

Funding Notes

Stipend: c. £19,000 (in line with the UCL rate) + UK fees

Duration of Studentship: 3 years fees and 3.5 years stipend

Start date: September 2023