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Quantitative structure-retention relationships models to predict gradient high-performance liquid chromatography retention time

Applications are invited from high-calibre and passionate students seeking to pursue an exciting career in analytical science research, and particularly to help more rapidly improve the analytical method development process through incorporation of new in silico models.

The development of separations-based analytical methods is often time consuming and requires significant experience to optimise successfully. With much improved capability to selectively separate and detect hundreds to thousands of chemicals in complex mixtures, computational approaches including machine learning, have been used to predict method parameters in chromatography using quantitative structure-retention relationships (QSRR). The aim of this PhD collaboration is to create QSRR models to predict gradient retention behaviour for a large and diverse library of >1000 small molecules with the view to streamlining the method development process in silico. It will involve a significant portion of time training in the development and application of liquid chromatography mass spectrometry-based methods in particular, including using triple quadrupole and high-resolution time-of-flight mass analysers.

Based in and co-funded by the College of Health Medicine and Life Sciences (CHMLS), GlaxoSmithKline (GSK) & the Community for Analytical Measurement Science (CAMS) the studentship offers an annual stipend of approximately £20,551 (including London weighting) plus Home/EU tuition fees and bench fees for a maximum of 42 months.

The start date will be 1st October 2023.



This project will offer a challenging, collaborative and transdisciplinary opportunity for an excellent PhD candidate to work at the forefront of this exciting area with leading academic teams at Brunel University London and Imperial College London and in partnership with GlaxoSmithKline (GSK), a globally leading pharmaceutical company. All three teams have extensive track records in this area and will provide complementary and collaborative training in analysis using liquid chromatography-mass spectrometry and advanced chemometrics. As part of the project, students will be expected to spend at least three months at a GSK site in the UK providing a unique industry experience and training opportunity.

Based primarily within the Environmental Science Division at Brunel University London, the successful candidate will have access to newly opened £2 million research labs hosting multiple analytical platforms including LC-MS, GC-MS and 2D LC systems. The student will benefit from training and development in the use of machine learning, data analytics and visualisation to enable cross method and platform retention prediction for small molecules.

The student will also benefit from access to research facilities at Imperial College London through the Environmental Research Group at its brand new state-of-the-art laboratories at the £2 billion Imperial White City Campus ( The successful candidate will have access to its new dedicated high throughput mass spectrometry suite.

The successful candidate will be supervised by an expert interdisciplinary team of academic and industrial researchers who will provide full training for the research:

Supervisory team

  • Dr Thomas Miller, Lecturer in Environmental Sciences, Department of Life Sciences, Brunel University London (London, UK).
  • Dr Leon Barron, Reader in Analytical & Environmental Sciences, School of Public Health, Faculty of Medicine, Imperial College London (London, UK).
  • Dr Azzedine Dabo, Investigator, Analytical Platforms & Platform Modernisation – Method Innovation Team, GSK (Stevenage, UK).

For informal discussions, please contact Dr Thomas Miller (


Candidates should have an undergraduate degree (first or upper second class) or equivalent qualification in analytical science, biochemistry, environmental science, computational science, pharmaceutical science or toxicology. A Masters qualification in a relevant area would be desirable. Knowledge of analytical chemistry, coding and machine learning is desirable, ideally with research experience. Applicants who have not been awarded a degree by a University in the UK will be expected to demonstrate English language skills to IELTS 7.0 (minimum 6.5 in any section).


How to apply

If you wish to apply, please e-mail the following to by 30th June 2023.

  • An up-to-date CV.
  • A single-page A4 single-spaced personal statement describing why you are a suitable candidate (i.e. outlining your qualifications and skills).
  • One example of your academic writing (e.g. an essay, a section from a dissertation).
  • Names and contact details for two academic referees.
  • A copy of your highest degree certificate and transcript.
  • A copy of your English language qualification, where applicable.

Short-listed applicants will be required to attend an interview week commencing Week of 17th July 2023. The successful candidate will be instructed to submit a formal online application via Admissions.

For further information about how to apply, please contact the College of Health Medicine and Life Sciences Postgraduate Research Office on

Meet the Supervisor(s)

Thomas Miller - As an interdisciplinary scientist with a background in biology and analytical chemistry, my research interests are focussed on the impact of chemicals in the environment and the interaction this chemical stress has with other environmental stressors. My expertise lies in small molecule mass spectrometry to determine chemicals found in the environment (especially in wildlife) and to determine biomarkers and pathways associated with adverse effects in exposed organisms. I am also interested in the integration of artificial intelligence within environmental toxicology to support and solve different environmental challenges.  From the start of my PhD at King's College London my research was originally focussed on the uptake, biotransofrmation and elimination of pharmaceuticals in a freshwater invertebrate (Gammarus pulex) commonly found in UK rivers. I developed and validated machine learning models to predict these proccesses to support and potentially replace bioaccumulation testing during environmental risk assessments. I then moved into a postdoctoral position where I focussed on understanding the impact of pharmaceuticals by assessing behavioural disruption in these organisms. I developed and applied metabolomic workflows to gain a mechanistic understanding of animal behaviour and to link cause-effect relationships for different drug exposures. Here at Brunel, I will be working in three main areas concerned with chemical pollution. First is concerned with the determination of chemicals (and mixtures) using exposomics to characterise the chemical space in the environment, with a focus on internalised residues in animals. Second, improving mechanistic understanding of cause-effect relationships using metabolomics and lipidomics to determine biochemical changes that are phenotypically anchored. Finally, development and application of AI to support envrionmental risk assessment, replace animal testing and improve interpretation of complex datasets to better understand animal health.