Senior Postdoctorial Research Assistant – PREDICTS2

Brief Summary of Role
This NERC-funded project aims to collate data from published before-and-after comparisons of how sites’ ecological assemblages changed when land use changed, and to integrate them with new datasets on species’ functional ecology and phylogeny in order to estimate how global land-use change affects local biodiversity, ecosystem structure and ecosystem function. It builds on the successful PREDICTS project (www.predicts.org.uk), extending it by considering ‘biotic lag’ and ecosystem function.

The role involves extending the PREDICTS database schema to hold before-and-after comparisons, estimating phylogenies for major taxa in the database, developing a reproducible workflow for statistical analyses (mixed-effects models and phylogenetic comparative models) and using it to analyse the data, writing manuscripts and reports, and co-supervising project students working on the project. The post is based with Prof Andy Purvis at the Natural History Museum but with one day per week at UNEP-WCMC in Cambridge.

Ideally this person would start with us no later than October 2015.

Salary: Up to £34,000 per annum (dependant on experience) plus benefits

Closing Date: Midnight on Sunday 23rd August 2015

Interviews: Stagnated interviews: Early round of submissions will interview week commencing 17th August and closing submissions will interview week commencing 7th September 2015

Role competences:
BEFORE beginning your application - Please read the section below about the ‘Online Application Process’ carefully as we will ask you to attach a CV and Covering Letter.

If you wish to be considered for this role you will need to address all of the following competences in your ‘cover letter’:

1. PhD in statistical conservation ecology or a similar discipline.
2. Up to 4 years of postdoctoral experience.
3. Statistical modeling of complexly-structured data (mixed-effects models)
4. Phylogenetic comparative analyses
5. Phylogeny estimation
6. Functional trait approaches to ecology/biodiversity
7. Database design
8. Ability to evaluate biodiversity survey design
9. Experience of analysing time-series data
10. GIS, using both local and global data
11. R programming essential
12. Python scripting desirable
13. Reproducible workflows
14. Writing publication-quality manuscripts
15. Supervision of project students and junior staff
16. Experience of science-policy interface
17. Experience of working in a large team