Research Associate

Updated: about 2 months ago
Location: Strand, ENGLAND
Job Type: FullTime
Deadline: 24 May 2017

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Research Associate


Reference: THW/17/059639/000546
Salary Details: £32,958 - £39,324 per annum
Allowances: £2,623 London Allowance
Contract Type: Temporary/Fixed term
Contract Term: Full time

The work required encompasses the exploitation, evaluation and potentially derivation of Land Surface Temperature (LST) data from Earth Observation (EO) satellites, primarily geostationary but also polar-orbiters. It will also encompass the use of other EO data products of relevance to crop pest and disease forecasting, and related topics of importance include the estimation of LST at times of cloud cover, where for example use of surface energy balance models maybe required, and the estimation of sub-pixel characteristics such as separate canopy and soil temperatures. Work will commence by testing existing LST products delivered by Space Agencies against “ground-truth” data, but the potential exists to develop optimised products using adjusted algorithms.
The work stretches across two collaborative projects funded, respectively, by the Newton-China Agritech Programme and the UK Space Agency International Partnerships Programme. Work in China is focused primarily on supporting the development of locust control using bio-pesticides as an alternative to chemical spraying, whereas in sub-Saharan Africa it encompasses a wider range of pests that pose a risk to locally grown crops and where the end product is a real-time information system that will feed actionable information to smallholder farmers. Both projects wish to use satellite-derived LST data, alongside other EO products, as a key input. The overall project duration is 5 years (UKSA-IPP project).
You must have experience with assessment of larger data sets and coding development – ideally Python. You will be an enthusiastic scientist, with an interest and wider knowledge of environmental science, and must have experience with manipulating larger EO data sets and skills in coding development – ideally in Python. Experience with land surface temperature data is highly desirable, as is some experience of fieldwork. Some knowledge of helping to set up field monitoring stations for the collection of environmental information (in this case LST) will also be relevant to parts of the work, as will algorithm development and the derivation of spectral emissivity measures. It is not expected that a single post holder will possess all the skills nor cover all activities that these two projects require, and a second recruitment is expected to be conducted subsequently to cover the remaining activities and/or skills.
We post will be based within the Department of Geography, King’s College London, located in the heart of London (UK). In the latest world QS rankings of Universities and Departments, King’s Geography was placed 18th in the world and in the latest UK Research Excellence Framework, almost 80 per cent of our research was rated as internationally excellent or world leading in quality.
The work will involve periods of international travel and strong co-operation and collaboration with UK and international partners, as well as project reporting to funding agencies and the writing of scientific papers to present findings from the work.
For an informal discussion to find out more about the role and the projects it contributes to please contact Professor Martin Wooster at
martin.wooster@kcl.ac.uk
The deadline for applications is midnight on 24th May 2017, with interviews expected within 2 weeks.
Please note that this is a PhD level role but candidates who will have submitted their thesis by the time of their appointment and are awaiting viva/award of their PhD will be considered for interview.
Should the successful candidate be awaiting the award of their PhD, the appointment will be made at Grade 5, spinal point 30 with the title of Research Assistant until confirmation of the award of the PhD has been received. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6, spine point 31.

Closing date: 24 May 2017

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