Post-doctoral opportunity: Research Associate in Earth Observation and Landscape Fire Science

Post-doctoral opportunity: Research Associate in Earth Observation and Landscape Fire Science
University: King’s College London
 £40,386 – £47,414 per annum, including London Weighting Allowance
 Social Science & Public Policy
 Department of Geography
 Martin Wooster,
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Job description

The researcher will join the Leverhulme Centre for Wildfires Environment and Society and be based within the vibrant, multi-disciplinary Earth Observation and Wildfire Research Group at King’s College London.  The postholder will undertake their own research, and will also act with Professor Wooster as capability lead for Earth Observation in the Leverhulme Centre – helping to support and advise other scientists across the four institutions involved in their use of EO data and methods.  The postholders research should focus on scientific investigation of landscape burning using methods that include satellite EO, for example to study fire and its changing controls, magnitude and impacts. The postholder will not be expected to necessarily develop new remote sensing algorithms, but will rather address questions about anthropogenic and naturally occurring biomass burning regionally and globally. Within this framework the postholder will have some leeway to forge their own research path. Within the post there are opportunities to work on airborne EO and other field projects related to wildfire, including a deployment to Canada in 2023, and to be involved with public engagement, and science communication activities.
This post will be offered on a full-tome, fixed term contract for 48 months.

Key responsibilities

  • Leading and contributing to research that delivers an improved scientific understanding of landscape fires and their controls and impacts, in particular using approaches that involve Earth Observation (EO) science
  • Advising other members of the Leverhulme Center on the use of appropriate remote sensing datasets and methods, acting as a Capability Lead in this area.
  • Typical tasks will include:
  • helping develop the use of EO methods and data within the centres research
  • contributing to accuracy assessment and improvement efforts for satellite remote sensing products related to landscape fire
  • developing code and tools for spatial data processing and analysis that support the centres science programme
  • leading and contributing research focused on key topics in landscape fire science, primarily in relation to tropical and high latitude fires and/or global fire regimes
  • Writing and submission of scientific publications
The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post. 

Skills, knowledge, and experience

Essential criteria
1.       PhD in physical, geographical, earth or environmental sciences
2.       Strong scientific record of research activity and publication
3.       Expertise in satellite Earth Observation, and some knowledge of landscape fire/wildfire science
4.       Strong computing skills – including accessing, processing and analyzing spatial data (in formats such as HDF, NetCDF, GeoTIFF, shapefiles etc) ideally with Python (e.g. using numpy, scipy, pandas, xarray, skimage) – as well as potentially software such as GDAL, ENVI, QGIS, NCL.
5.       Ability to write efficient code (well documented with good version control) to support data processing and scientific analysis
6.       Strong analytical, quantitative analysis and scientific interpretation skills, as well as ideally of research questions in landscape fire science (with respect to land or atmosphere)
7.       Good written and verbal communication and the ability to collaborate and support others in their use of EO methods and products
Desirable criteria
1.       Experience with HPC environments
2.       Experience with fire-related satellite EO datasets (active fire, burned area, atmospheric concentrations)
3.       Experience with machine learning and/or deep learning
Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6.
The selection process will include providing example of your written scientific work (e.g. report or paper), an interview and presentation, and a small coding task.