Salary range: £37,694 - £39,906 per annum
FTE: 1 (35 hours per week)
Term: Fixed (18 months)
Closing date: 10/08/2025
This post-doctoral research associate position will support the research aims of the CHALKY (Coccolithophore controls on ocean alkalinity) project. The main purpose is to develop and apply a spectral matching algorithm to improve retrieval of water quality parameters from ocean colour imagery over coccolithophore blooms. The project has already collected a unique and extensive set of in situ observations collected in the Iceland Basin of the Atlantic Ocean. You will complete data processing and quality control on in situ optical measurements of inherent optical properties and radiometry. You will then derive concentration-specific IOPs that will be used to populate a new bio-optical model for coccolithophore-rich waters. Radiative transfer simulations will be used to establish optical closure between in situ IOPs and radiometry and to populate a database of remote sensing reflectance signals that will inform development of a new spectral matching algorithm for operations in this water type.
You will work with Prof David McKee who is the local Principal Investigator on this NERC-funded project. You will play a leading role in supporting PhD students working in the Marine Optics and Remote Sensing (MORSE) research group, based in our Physics Department. The project involves close collaboration with partners across the UK and the USA and will contribute to the NERC Biocarbon Programme. The MORSE group has been active in the field for over 30 years and provides access to state of the art experimental and computational facilities, including advanced radiative transfer simulation software and next generation in situ optical instrumentation.
The successful candidate will have considerable and relevant experience working in marine optics and remote sensing or similar areas of research. They will be able to work with large data sets, process and interpret remote sensing imagery and have experience of working with in situ optical sensors and associated data. They will have good coding proficiency, preferably with direct experience of Python and/or Matlab. Knowledge of coccolithophore biology / ecology would be helpful.
For any informal enquiries, please contact Professor David McKee ([email protected]).
