About the Project
This project is one of a number in competition for a studentship from the Faculty of Engineering and Design at the University of Bath. If successful, this studentship is expected to commence 28 September 2026.
An alternative start date may be possible if agreed with your intended supervisors and the Doctoral College.
Project Background:
Traditional chemical process development typically focuses on optimizing performance metrics such as yield, rate or selectivity. Sustainability considerations–including waste generation, solvent choice and energy/water consumption–are often addressed once an efficient process has already been identified. This PhD project aims to re-imagine that workflow by embedding sustainability directly into the optimization process from the outset.
The project will develop a multi-objective optimization framework capable of balancing traditional reaction outcomes with sustainability-oriented targets.[1] By incorporating quantitative sustainability metrics–such as E-factor or process mass intensity (PMI)–alongside yield or selectivity, the project will enable chemical processes to be optimized simultaneously for both performance and environmental impact. Reaction spaces will be designed to prioritize greener solvents, sustainable reagents and reduced waste generation.
Central to this approach is the use of Bayesian optimization and other similar machine learning algorithms,[2] which efficiently navigate large and complex reaction spaces using only a small number of experiments. These methods allow for data-driven decision making, where each experimental result directly informs the next most promising conditions to test. In addition, model interpretation techniques–such as feature importance and SHAP analysis–can provide chemical insight into which parameters most strongly influence both reactivity and sustainability outcomes.[3]
The methodology will be initially demonstrated on benchmark chemical systems (such as catalytic processes) to evaluate the optimization process with respect to multiple competing objectives. The broader goal is to establish a general workflow for sustainable process development that can be extended to a wide range of chemical processes and industrial contexts (such as synthesis of pharmaceuticals).
The project offers an exceptional opportunity to work at the interface of Digital Chemistry, automation and sustainability. Experience will be gained in experimental design, reaction optimization, computational modelling and data science–key skills for the next generation of chemical researchers.
Candidate requirements:
Applicants must have, or be about to obtain, a UK Honours degree 1st or 2.1, or international equivalent. A master’s level qualification would also be advantageous.
Non-UK applicants, who are not currently studying in the UK, must meet the programme’s English language requirement before the application deadline – no exceptions will be considered.
We encourage applications from highly motivated students who hold (or expect to hold) a first-class or upper-second-class degree in Chemistry, Chemical Engineering or a related discipline. Experience of coding, computational chemistry and machine learning/AI is not essential. Full training in all necessary computational methods, coding and machine learning will be provided as a core part of the studentship.
Enquiries:
Informal enquiries are encouraged! Direct these to Dr Jamie A. Cadge.
Deadline: 30th November 2025.
For more information and to apply visit https://www.findaphd.com/phds/project/bath-engineering-and-design-studentships-sustainability-driven-chemical-process-optimization/?p188023
