Senior Research Associate (RA1623)
Vacancy Details
| Summary | |
|---|---|
| Location: | TYN-ENV - Tyndall Centre - ENV |
| Job Type: | Full-Time |
| Category: | Research & Analogous (RA) |
| Closing Date: | 06/06/2019 |
| Date Posted: | 05/08/2019 |
| Reference: | RA1623 |
Description
Faculty of Science
School of Environmental Sciences
Senior Research Associate - Ref: RA1623
£33,199 to £39,609 per annum
Applications are invited for the post of Senior Research Associate under the supervision of Professor Corinne Le Quéré, to conduct research on the development and application of Machine Learning approaches to global carbon cycle modelling of the ocean. The post-holder will apply Machine Learning approaches to quantify the growth rates of different types of marine plankton as a function of environmental conditions, and use the model to explore the response of marine ecosystems to climate change and other environmental changes. The post holder will be part of a carbon cycle modelling team and will work in collaboration with international networks.
The post will be part of the Royal Society Research Professorship of Prof Le Quéré. The post holder will have experience of independent research on the development and application of machine learning approaches to any science, and interest in its application to carbon cycle, climate change and Earth System sciences.
The researcher will bring new expertise to an existing carbon cycle research group. The post-holder will be expected to work with international researchers and develop networks with holders of key data, assisting in the definition of data needs and in the integration of new data technology (e.g. automated imaging) to gain insight into environmental changes.
The research will be based at the University of East Anglia in the School of Environmental Sciences.
A PhD in science (or equivalent experience), along with previous experience of independent research in the development and application of machine learning approaches and in the analysis, synthesis and interpretation of observations and model results to any science. You will have an interest in applications of Machine Learning to carbon cycle and climate change sciences. You should be able to work in a team and conduct independent research. You will also have excellent oral and written communication skills, and coordination skills; and be able to meet all the essential criteria set out in the Person Specification. You do not need prior knowledge of carbon cycle science to apply for this post.
The full time post is for a fixed term of 33 months available from 1 July 2019, or as soon as possible thereafter.
Closing date: 6 June 2019.
The University is a Bronze Athena Swan Award holder, currently working towards Silver
Downloads:
| Contacts | |
|---|---|
| Contact 1 | |
| Contact Name: | Professor Corrine Le Quere |
| Telephone: | 01603 592840 |
| Email Address: | c.lequere@uea.ac.uk |