VACANCY INFORMATION
The UCL Department of Chemical Engineering is one of the top research and teaching departments in the UK and has world-class standing. The department offers undergraduate and postgraduate programmes and has an extensive research portfolio across a wealth of areas, from the molecular scale to the scale of industrial plants. It hosts 28 academics whose research is collaborative, ground-breaking and focused on solving societal problems.
The department is seeking an enthusiastic and dedicated PhD student to develop fundamental and data driven models for purification methodologies for the rapid development of manufacturing processes for advanced synthetic drugs and drug products. The project has significant industrial relevance as it is part of a large collaboration between UCL, Imperial College London and Eli Lilly and Company, a global pharmaceutical company with strong focus on research and development (R&D), funded by an EPSRC Prosperity grant entitled “Transforming synthetic drug manufacturing: novel processes, methods and tools”. The post-holder will have the opportunity to share ideas and results with the industrial and academic partners and to collaborate with the industrial R&D team.
The post is funded for 4 years.
STUDENTSHIP DESCRIPTION
The research vision of the EPSRC Prosperity project is to deliver novel systems-based engineering design methods for the rapid development of manufacturing processes for advanced synthetic drugs and drug products, strongly rooted in scientific understanding and building on state-of-the-art manufacturing technologies, explainable AI, modelling and experimental approaches. The outcomes of the work will be a set of technologies, techniques, methods and tools that will enable faster and cheaper development, design and manufacturing of advanced therapeutics.
The focus of the work at UCL is to develop a modelling framework for High Performance Liquid Chromatography (HPLC) to predict the retention behaviour of small molecule drug candidates. Successful computational approaches will save valuable drug product, and allow quick and cost efficient HPLC method development. The models should consider the materials (resin, eluent) properties as well as the HPLC operation. Both fundamental first principles models and data driven retention models will be applied as well as hybrid models. The first principle models will focus on Solvation Energy Relationships (SER), whereas data driven models target Quantitative Structure-Retention Relationships (QSRR) combining machine learning, model predictive design of experiment strategies and mining of historical HPLC data.
The post-holder will learn how to develop mathematical models based on first principles as well as machine learning methods and how to interpret model results, particularly in relation to model validation to uncertainty in the input experimental data. The techniques and skills they will learn are directly transferable to technological problems relevant to many other industrial sectors.
The post-holder will present the research results at international conferences and in peer-reviewed journal articles of high international standing.
PERSON SPECIFICATION
The successful candidate will have completed or be near to complete a first-class degree at the MEng or MSc level in Chemical Engineering or a related discipline.
The successful candidate will be a dedicated student, preferably with advanced understanding of mathematical modelling of chemical engineering processes, some knowledge of machine learning and with good understanding of scientific research methods.
Willingness to perform independently within a collaborative environment is a must.
Demonstrable knowledge of research methods and of mathematical modelling is desirable but is not a necessary requirement.
ELIGIBILITY
First-class degree at the MEng or MSc level is required. We actively encourage the application of female applicants or applicants from underrepresented groups for this position.
UK and Overseas students are eligible to apply.
To apply, please submit your full application through the following link: http://www.ucl.ac.uk/adminsys/search/
Please nominate Prof. Eva Sorensen as academic supervisor in your application form and include a statement of interest.
For informal enquiries please contact Prof. Sorensen: [Email Address Removed]
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