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Current NCEO career openings

Research Associate Earth Observation Science and Biomass Burning
Reference: THW/18/059639/001041
Salary Details: Grade 6 £33,518 – £39,992 per annum
Allowances: plus £3,223 London Allowance
Contract Type: Temporary/Fixed term
Contract Term: Full time
Based at King’s College,the Strand Campus, the post lies within the research team of Professor Martin Wooster and is focused on use of various satellite fire products in scientific studies. The post holder will lead on some studies, and in others they will contribute to the work led by other team members. Applicants must be able to work with large satellite EO datasets, with products related to biomass burning, be able to write well documented, shareable computer code (ideally in Python), and demonstrate skills in planning, executing and writing up high quality scientific studies. Experience in linking satellite data to environmental models is advantageous but not mandatory. The post will be affiliated to the National Centre for Earth Observation (NCEO), and this role will help form part of NCEO’s commitment to delivering National Capability in Earth Observation science to NERC (Natural Environment Research Council).

The selection process will include a panel interview. Interviews are planned to be held shortly after the closing date.

The closing date will be 28 August 2018.

For information and to apply click here
PhD in Arctic sea-ice reduction: gaining new knowledge from data assimilation

Available at the University of Reading, details here

The strong decline of Arctic sea ice is a conspicuous indicator of climate change: the last 13 years (2005-2017) have seen the 13 lowest September Arctic ice extents in the satellite record. The dramatic reduction in extent has been accompanied by an even stronger decrease in volume, as measured by satellite altimetry and field observations.

Arctic sea ice is an important component of the global climate system: its high albedo (reflectivity) relative to ocean water significantly affects the surface radiative budget; it forms a mechanical barrier to transports of heat, moisture and momentum between the atmosphere and ocean; and the formation and melt of sea ice acts as a buoyancy forcing to the ocean, affecting deep-water formation and the thermohaline circulation.

The mechanisms responsible for changes in Arctic sea-ice volume and its distribution are complex, involving both thermodynamic changes and dynamics of motion and deformation. Understanding the local causes of past sea-ice loss is crucial to developing the ability to predict the future of Arctic sea ice, and its impacts on the broader climate system.
This project will combine new and emerging satellite estimates of sea ice thickness with a state-of-the-art sea ice model using data assimilation. Data assimilation is a tool that uses observations to improve the model estimate of the sea-ice state, which can in turn be seen as a way of filling in the gaps in the observational record. Using these tools, we will develop a sea ice reanalysis (spatio-temporal description of the sea-ice state) that will be analysed to identify the proximate causes of sea-ice change over the satellite era, giving us new physical insight.

The Lead Supervisor will be Professor Danny Feltham, Co-supervisors Professor Peter Jan van Leeuwen, Professor Andy Shepherd


PhD in Machine Learning and Earth Observation Data Analysis within an African Crop Pest Risk Information Service.

A 5-year multi-million pound project funded by the International Partnership Program of the UK Space Agency is currently working to create a Pest Risk Information Service (PRISE) to help reduce yield losses to crop pests and diseases in Africa farming. This system will use Earth Observation, meteorological and in situ data to provide real-time pest risk forecasts to smallholder farmers, helping them take preventive action and increase resilience to pest outbreaks. The core of the PRISE system is a series of biological models of pest development driven by satellite Earth Observation, and the PhD will explore the potential utilization of machine learning within the these and will test whether they may perform better than bio-physical and statistical modelling. The project is a case studentship between King’s College London and Assimila Ltd and the student will also receive regular input from CABI who are the partners with responsibility for the biological models of pest development. In addition to satellite remote sensing, machine learning and modelling skills, fieldwork and travel to a number of African countries working with PRISE will be a significant component of this PhD.
CASE partner: Assimila Ltd (UK SME in Reading). CABI will also be a non-CASE partner.
Main supervisor: Prof. Martin Wooster (Dept of Geography, King’s College London)

More details are here