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Funded PhD studentship in machine learning approaches to understand the uptake and elimination of anthropogenic stressors in animal health

Anthropogenic stress in the form of chemical pollution can have a significant impact on animal health for both wildlife and farmed animals. A mounting number of published studies have demonstrated that chemical contaminants have potential to cause adverse impacts for animal health at all levels of biological organisation. Many chemicals are not studied with regards to their potential uptake, biotransformation and elimination in relevant organisms, restricting predictions of risk and understanding of potential mechanisms of toxicity. It is also critical that we investigate mechanisms of uptake and elimination to enable translational understanding across different species and reduce the requirements for multiple species testing.

The proposed research will generate toxicokinetic data from fish and invertebrates, then utilise AI to predict uptake and elimination kinetics for multiple chemicals. Feature data and model parameters will be extracted to understand structural motifs that affect uptake, biotransformation and elimination in these organisms. The AI approaches will use transfer learning to identify the translational potential of the developed model to enable AI read-across into both other fish and invertebrate species. The tools developed here will therefore support the potential for replacement of animals during chemical testing.

This project is an interdisciplinary collaboration between Brunel University London and AstraZeneca, a major global pharmaceutical industry company that is committed to assessing and managing potential risks of pharmaceuticals in the environment.

As part of the studentship the successful candidate will go on a placement with AstraZeneca lasting between 3-18 months. The student will receive extensive multidisciplinary training in techniques to develop a valuable set of technical and theoretical expertise for a successful career as an independent scientist. The work will be approximately a 50:50 split between ‘wet lab’ and computational analysis that will develop the students’ capabilities and skillset in both areas. Importantly, the student will receive in-depth training in data science, programming and artificial intelligence that will develop their digital literacy. Alternatively, candidates that have a relevant background in maths and/or data analytics that would like to develop biological knowledge will also be suitable for this position. The graduate school at Brunel offers multiple workshops and events to develop transferable skills including communication, writing, leadership and management that will complement skills gained through the proposed research project.

The successful candidate will be supervised by Dr Thomas Miller. For informal discussions about this studentship, please contact Dr Thomas Miller (thomas.miller@brunel.ac.uk).

Eligibility

Candidates should hold an undergraduate degree (first or upper second class) or equivalent qualification in either a bioscience or analytical science discipline. A Masters qualification in a relevant discipline would be desirable but not essential. Prior experience in data analysis/visualisation, machine learning or analytical chemistry would be beneficial for the position. 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 6.0 in any section).

 

How to apply

If you wish to apply, please e-mail the following to chmls-pgr-office@brunel.ac.uk by 26th June 2021.

  • An up-to-date CV.
  • A single-page A4 single-spaced personal statement setting out why you are a suitable candidate (i.e. outlining skills, experience and motivation).
  • One example of your academic writing (e.g. an essay, a section from an undergraduate or a Masters 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. Applicants chosen for interview will be instructed to submit a formal online application via Brunel Admissions.

For further information about how to apply, please contact the College of Health and Life Sciences Postgraduate Research Student Office on chmls-pgr-office@brunel.ac.uk

 


Meet the Supervisor for this Studentship

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.