Salary - £37,405 to £39,694
Hybrid working (50/50)
Permanent, full-time
Closing date for applications, Wednesday 13th November 2024. We reserve the right to close this advert early if we find the right candidate, so we encourage you to apply early.
UKCEH is looking for an experienced Quantitative Ecologist to join our team of talented individuals, contributing to scientific discovery and generating the data, insights and solutions that researchers, businesses and governments need to solve complex environmental challenges.
Working at UKCEH is rewarding. Our science makes a real difference, enabling people and the environment to prosper, and enriching society. We are the custodians of a wealth of environmental data, collected by UKCEH and its predecessors over the course of more than 60 years.
As a valued member of our team, you’ll get:
1. 27 days annual leave, rising to 29 days after five years, plus 3 days for our Christmas closure
2. 10% employer pension contribution
3. Enhanced maternity and paternity leave (subject to qualifying requirements)
4. 24 hour, 365-day access to support with physical, mental, social, health or financial issues plus access to our trained Welfare Officers
5. Flexible working opportunities
...
You’ll be helping us to generate new policy-relevant science and data products, from the integration of spatiotemporal datasets with environmental data, including ecological observations from field surveys. You’ll have the opportunity to collaborate across a varied range of projects will skilled Earth Observation practitioners, statisticians, field-ecologists and application/software developers.
The main focus of your work will be testinghypotheses about the relationshipsbetween environmental drivers and ecological responses. In doing so you will help generate new understanding of the causes and consequences of change in habitats, vegetation and soils across the globe.
This work will help us understand and more accurately forecast ecological responses to drivers such as climate change, land-use change, pollution and changing land management practices.
If you are enthused by this research agenda and think you contribute to our teamwe want to hear from you. Ideally, you’llhave experience combining your ecological understanding and scientific programming skills in Python and R.
You’ll be joining a leading independent, not-for-profit research institute that’s committed to recruiting talented people like you, progressing your career and giving you the support you need to thrive at UKCEH.
Your main responsibilities will include:
6. Undertaking integrated analysis of spatial data with ecological observations, generating novel insights into the causes and consequences of ecological change.
7. Analysing changes in habitats and landscape features and their related biodiversity over time.
8. Using different data sources to provide biodiversity relevant information on habitat extent and condition.
9. Understanding how landscape structure relates to habitat condition at different scales.
10. Using the understanding gained to develop and apply predictive models of biodiversity to future scenarios of global change.
11. Publication of datasets and peer reviewed journal papers.
For the role of Quantitative Ecologist, we’re looking for somebody who has:
12. A PhD in relevant subject, or a Masters and experience equivalent to a PhD in a relevant subject.
13. Experience in the application of remotely sensed data for ecological analyses.
14. The ability to lead and write clear scientific reports and peer reviewed papers.
15. Used programming languages to solve complex geospatial or scientific problems; for example,C, C++, Java, Python, R.
16. Experience manipulating and analysing spatial data, for example, using GDAL, R, Python or equivalent
17. Can develop analytical workflows.
18. Presented work clearly to expert and non-expert audiences.
19. Data manipulation and data management skills.
If we’ve just described you, we’d love to meet. Apply now.
Unfortunately, we are unable to offer visa sponsorship for this position at this time.