ndustrial Sponsored PhD: A ‘Lab on a Bench (LoB)’ setup equipped with Machine Learning (ML) as a high throughput (HTP) for formulation design and validation.

Most of chemical products consist of multiple compounds that are formulated together, of

which the development process is iterative, laborious, and complex. The formulated products

sector, contributing over £149bn to the Gross Value Added of the UK economy, requires

innovative method to accelerate the innovation pace and to enhance the corresponding

sustainability profile.

The aim of this project is to develop a highly innovative ‘Lab on a Bench (LoB)’ setup,

integrated with Machine Learning algorithm, as a high throughput method for screening and

developing formulated products that are used in a broad range of applications. The project

has the following objectives: i) to further develop the LoB setup that incorporates

microfluidics with miniaturized characterization techniques; ii) to validate the accuracy and

efficiency of the LoB method; iii) to integrate the ML algorithm with the LoB operation,

forming a closed feedback loop; iv) to apply the LoB setup in investigating a range of

formulated products with high throughput efficiency. The knowledge will be used to guide

the development of new formulated products.

Working closely with the industrial partner Croda, the doctoral candidate will develop a wide

range of skills in colloidal and interface science, computer science, and instrumentation,

establishing a broad appreciation of formulation engineering. They will develop a portfolio of

transferrable skills such as project management, communication and team working, which

ensures excellent employability upon completion of the project.

If you have a background in Chemistry, Physics, or Chemical Engineering and are passionate

about sustainability and instrumentation, this is an excellent opportunity.