Research Associate in Geography (King's College London)

# Job description King’s Earth Observation and Wildfire Research Group undertake cutting edge research that includes the study of global landscape fire and its role and impact on Earths’ environment and on human health ( [https://wildfire.geog.kcl.ac.uk/](https://wildfire.geog.kcl.ac.uk/)). They are looking to appoint an enthusiastic, skilled and dedicated researcher with expertise in satellite Earth observation to work within the team – primarily on a project helping to further develop the high-profile Global Fire Assimilation System (GFAS) operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) ( [www.ecmwf.int/en/forecasts/dataset/global-fire-assimilation-system](https://www.ecmwf.int/en/forecasts/dataset/global-fire-assimilation-system) ), but also contributes to other projects. GFAS provides near-real-time emissions of gases and aerosols from biomass burning through data assimilation of multi-satellite Fire Radiative Power (FRP) retrievals – and is used to supply fire emissions data to the Copernicus Atmosphere Monitoring Service ( [https://atmosphere.copernicus.eu/](https://atmosphere.copernicus.eu/)), as well as by many external users. The role will provide opportunities to collaborate with researchers within the Kings’s Group on other projects as well, and to work closely with the CAMS group in ECMWF and externally. The researcher will work with multiple sources of FRP data from polar-orbiting and geostationary and satellites – and may also use burned area, trace gas and landcover data to also further development and testing of GFAS. There will be the opportunity to use GFAS and other datasets to help address important questions about fire, environment, air quality and health. Strong experience in use of large-scale datasets (preferably using Python) is essential for this position, ideally some strong experience of satellite remote sensing, and proven writing skill and ideally some peer-reviewed publications. Good time management and communication skills are required. This post will be offered on a full-time, fixed term contract until 28th Feb 2025.# Key responsibilities - Developing and/or using GFAS input and output datasets - Testing the quality of the GFAS outputs and their sensitivity to different inputs - Producing relevant documentation and reports on GFAS validation - Contributing to the development of the GFAS service - Contributing to answering questions from GFAS users - Writing up of scientific reports and publications for peer-review journals - Assisting other colleagues on varied projects with relevant expertise and input 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. Educated to PhD-level 2. Strong experience with processing and analyzing spatial data, including ideally satellite data, with an understanding of statistical analysis. 3. Ability to write efficient processing and analysis code in Python; incl use of packages such as numpy, scipy, pandas, xarray, skimage and data formats such as HDF, NetCDF, GEOTIFFS, shapefiles. 4. Excellent written and verbal communication skills Desirable criteria 1. Knowledge of wildfire remote sensing 2. Understanding of atmospheric transport 3. Experience with HPC environments 4. Some experience with data assimilation 5. Experience with GitHub 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. ## Further information The selection process will involve and interview, presentation and a short coding task. If you apply for this position please say you saw it on Physicaloxy

# Job description

King’s Earth Observation and Wildfire Research Group undertake cutting edge research that includes the study of global landscape fire and its role and impact on Earths’ environment and on human health ( [https://wildfire.geog.kcl.ac.uk/](https://wildfire.geog.kcl.ac.uk/)). They are looking to appoint an enthusiastic, skilled and dedicated researcher with expertise in satellite Earth observation to work within the team – primarily on a project helping to further develop the high-profile Global Fire Assimilation System (GFAS) operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) ( [www.ecmwf.int/en/forecasts/dataset/global-fire-assimilation-system](https://www.ecmwf.int/en/forecasts/dataset/global-fire-assimilation-system) ), but also contributes to other projects.

GFAS provides near-real-time emissions of gases and aerosols from biomass burning through data assimilation of multi-satellite Fire Radiative Power (FRP) retrievals – and is used to supply fire emissions data to the Copernicus Atmosphere Monitoring Service ( [https://atmosphere.copernicus.eu/](https://atmosphere.copernicus.eu/)), as well as by many external users. The role will provide opportunities to collaborate with researchers within the Kings’s Group on other projects as well, and to work closely with the CAMS group in ECMWF and externally.

The researcher will work with multiple sources of FRP data from polar-orbiting and geostationary and satellites – and may also use burned area, trace gas and landcover data to also further development and testing of GFAS. There will be the opportunity to use GFAS and other datasets to help address important questions about fire, environment, air quality and health.

Strong experience in use of large-scale datasets (preferably using Python) is essential for this position, ideally some strong experience of satellite remote sensing, and proven writing skill and ideally some peer-reviewed publications. Good time management and communication skills are required.

This post will be offered on a full-time, fixed term contract until 28th Feb 2025.# Key responsibilities

- Developing and/or using GFAS input and output datasets
- Testing the quality of the GFAS outputs and their sensitivity to different inputs
- Producing relevant documentation and reports on GFAS validation
- Contributing to the development of the GFAS service
- Contributing to answering questions from GFAS users
- Writing up of scientific reports and publications for peer-review journals
- Assisting other colleagues on varied projects with relevant expertise and input

*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.       Educated to PhD-level

2.       Strong experience with processing and analyzing spatial data, including ideally satellite data, with an understanding of statistical analysis.

3.       Ability to write efficient processing and analysis code in Python; incl use of packages such as numpy, scipy, pandas, xarray, skimage and data formats such as HDF, NetCDF, GEOTIFFS, shapefiles.

4.       Excellent written and verbal communication skills

**Desirable criteria**

1.       Knowledge of wildfire remote sensing

2.       Understanding of atmospheric transport

3.       Experience with HPC environments

4.       Some experience with data assimilation

5.       Experience with GitHub

*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.*

## Further information

The selection process will involve and interview, presentation and a short coding task.