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Research Fellow

University of Birmingham - School of Biosciences

Location: Birmingham
Salary: £41,526 p.a. Grade 7
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 13th May 2021
Closes: 16th June 2021
Job Ref: 96874

School of Biosciences, College of Life and Environmental Sciences

Location: University of Birmingham, Edgbaston, Birmingham UK

As this vacancy has limited funding the maximum salary that can be offered is Grade 7.37 salary £41,526.

Full Time/FTC for up to 23 months

Closing date: 16th June 2021

Please note, previous applicants need not apply.

To contribute to research on the impact of environmental pollution on biodiversity and human health. The post holder will have demonstrated skills in bioinformatics and artificial intelligence/machine learning to work on biological, chemical and environmental data collected from freshwater ecosystems. The ultimate goal of this research is identify targets for Ecosystem Services conservation, and pollutants prioritization for mitigation interventions. The post holder will ultimately contribute to make the environment a safer place.


Chemical pollution is recognized as one of the main causes of Earth’s ecosystem services deterioration and overuse, linked to the loss of biodiversity (ecosystems complexity and species richness). Ecosystem services directly affect human wellbeing and socio-economic welfare. Yet, we are unable to design preventive interventions that mitigate this loss and preserve natural resources because biodiversity loss happens on different spatial and temporal scales and dynamics are context-dependent outcomes from processes operating over many years. State of the art monitoring and prevention approaches fail to capture the complexity of causal links between pollution and biodiversity loss by studying the effects of unrealistic concentrations of individual compounds on indicator species and by taking only single snapshots of the long-term dynamics. These approaches are also largely correlative and missing, by design, potential pathological effects that may arise from chronic exposures to sublethal doses of chemical mixtures on ecosystems. Only by quantifying trajectories of abiotic and biotic systemic change before, during and after pollution events, can we begin to identify the causes of biodiversity and ecosystem services loss and, thus, implement informed preventive and remedial interventions that benefit the environment and humans, while enabling growth. 

The post holder will use biological, chemical and environmental data collected from sedimentary archives of watersheds to quantify past biotic and abiotic changes and establish causal links between chemical mixtures and systemic loss of biodiversity, ecosystem functions and services. Sedimentary archives preserve biological and environmental signals temporally, providing a continuous record of changes from pristine (unimpacted, dating back to pre-industrial revolution) to impacted environments.

Main duties

  • Carry out bioinformatics data analysis on environmental DNA extracted from lake sedimentary archives (metabarcoding and metagnomics);
  • Develop appropriate artificial intelligence/machine learning pipelines for the analysis of community dynamics through time and space;
  • Apply and/or develop machine learning algorithms to discover causal associations among biotic changes in communities (measured through eDNA), chemical pollutants (measure through mass spectrometry analysis) and environmental factors (e.g. Temperature, precipitations, etc.);
  • Develop machine learning algorithms for the prediction of biological communities change under future climate and pollution scenarios by training machine learning algorithms on long-term empirical data that incorporate biotic interactions;

Person Specification

  • PhD-degree in computer science, computational biology, bioinformatics or related fields with postdoctoral experience;
  • Fluency in bioinformatics, biostatistics and machine learning (at least two of these skills are required);
  • High level analytical and interpretation capabilities;
  • Knowledge of coding languages (e.g. python, R, C++);
  • Ability to communicate complex information clearly;
  • Ability to contribute to developing new frameworks in forecast modelling is not a requirement but will be considered a valuable skill;
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