Our rapidly changing world is placing critical ecosystems under unprecedented environmental pressures, pressure that includes exposure to a wide-range of chemical toxicants. The efficient protection of ecosystems requires knowledge of chemical toxicity. However, such information has to be obtained within the context of the 3Rs goal, i.e. the effort to Reduce, Refine and Replace the use of animals. Accurate prediction of species sensitivity to toxicants without chemical exposure experiments would represent a major step toward fulfilling this ambition. However, realising this aim requires a deep mechanistic understanding of relevant biological pathways, their conservation across species, and a framework to facilitate easy comparison and assessment. Fortunately, such objectives are now achievable, as rapidly increasing genomic resources contain a treasure trove of comparative data on the molecular components governing pollutant sensitivity. The overarching aim of this PhD is to harness genomic and other trait resources to deliver mechanistically informed estimates of toxicant sensitivity. This PhD research will make an invaluable contribution to understanding chemical effects on ecosystems without performing animal exposures.
The PhD student will obtain the skills and understanding necessary to generate in silico representations of organisms (termed ‘digital twins’) that will inform comparative ecotoxicological assessment. To generate such ‘digital twins’, the PhD student will investigate and design a system to integrate molecular information, species trait data and modelling tools within a modular framework. This will be done with the aim of producing an automated data infrastructure containing varied data types (e.g. genomic data, energetic and phenotypic traits), that can be used to rapidly retrieve cross-species information relevant to toxicant sensitivity predictions. Once the infrastructure is established, the PhD student will endeavor to develop approaches (e.g. using artificial intelligence) to help better predict the complex and multi-variate contributions made by various species characteristics to sensitivity. The output of such artificial intelligence approaches will be combined with established ecotoxicological data to validate links between species characteristics and observed sensitivity. Both the automated data infrastructure and the mechanistic insight it produces will increase the capacity of ecotoxicologists and environmental regulators to predict sensitivity in untested species.
The student will be registered and hosted by the Organism and Environment Division (OnE) of the Cardiff School of Bioscience (BIOSI), itself within the College of Biomedical and Life Sciences. The project will be undertaken in collaboration with the UK Centre for Ecology and Hydrology (UKCEH), a world-leading institute for research into land and freshwater environment and the Safety and Environmental Assurance Centre at Unilever. The student will follow the requirements of the Cardiff PhD programme, also benefiting from various training activities provided across the institutions involved.
Supervision and Partnership
Academic supervision will be provided by Professors Peter Kille (University of Cardiff) and David Spurgeon and Dr Stephen Short (UKCEH). The student will also be supervised by Dr Bruno Campos and Dr Claire Peart at Unilever.
The PhD student will be based primarily at Cardiff University but will be encouraged to visit UKCEH and make use of the opportunities provided by co-supervision across the different institutions. The successful applicant will also benefit from a longstanding partnership with the Safety and Environmental Assurance Centre (SEAC) of Unilever, the CASE partner organisation for this studentship. They will spend at least 3 months with partner specialists at Unilever learning about regulatory and operational aspects of ecological risk assessment and gain industrial knowledge and business insight.
This is a 4-year fully-funded BBSRC studentship. The studentship covers: (i) a tax-free stipend at the standard Research Council rate supplemented with an industrial uplift of £4K, (ii) tuition fees at UK/EU rate, and (iii) research consumables and training necessary for the project.
Applicants should have experience and/or a strong interest in bioinformatics, data analysis and computational science. We welcome applicants with a strong background in data science and interest in molecular biology. A background/interest in ecotoxicology is welcome but not required.
At least an upper second-class honours degree, or equivalent in any relevant subject that provides the necessary skills, knowledge, and experience, including environmental, biological, chemical, mathematical and computational sciences.
The studentships are available to UK and international students who meet the UK Research and Innovation residency requirements (to be residing in the UK for at least three years continuously prior to the beginning of the Programme).
There will be a virtual open day to answer any questions prospective candidates have at 14.00 pm on 12th of Sep Zoom Link https://cardiff.zoom.us/j/89364885784?pwd=S1htOThXejlleGV6R2xqN1ZjQW5mQT09&from=addon. Application link (https://cardiff.onlinesurveys.ac.uk/bbsrc-unilever-recruitment-2022-2) deadline is 9 am on 19st Sept and shortlisted candidates will be notified on 21th September.
Interviews will take place week starting 26th of Sept with shortlisted applicants being to travel to Cardiff (if practicable) for a hybrid interview since some panel members will be attending remotely. Prior to the formal interview, candidates will be asked to give a 5-minute presentation on a research project carried out by them.
Name: Professor Peter Kille
Email: [Email Address Removed]
Applications link: https://cardiff.onlinesurveys.ac.uk/bbsrc-unilever-recruitment-2022-2