Job title: Applied Statistician / Mathematician (KTP Associate)
Job reference: P66906
Application closing date: 22/04/2019
Location: Cornwall, UK.
Salary: The starting salary will be from £35,211 up to £43,267 on Grade F, depending on qualifications and experience.

This is a unique opportunity to work as the KTP Associate on a Knowledge Transfer Partnership<http://ktp.innovateuk.org/> between the University of Exeter and Chelonia Ltd. This post is available immediately, for 36 months, with the possibility of a permanent position within Chelonia after the project finishes.

Full details about the position, and a link to apply online can be found at the link here.<https://jobs.exeter.ac.uk/hrpr_webrecruitment/wrd/run/ETREC107GF.open?VACANCY_ID=067148OKSS&WVID=3817591jNg&LANG=USA>

For informal enquiries, please contact: Dr TJ McKinley (T.McKinley@exeter.ac.uk<mailto:T.McKinley@exeter.ac.uk>; 01326 259331) at the University of Exeter, Dr Nick Tregenza (nick.tregenza@chelonia.co.uk<mailto:nick.tregenza@chelonia.co.uk>; 01736 732462) at Chelonia Ltd.

Summary of the role/position

You will be employed by the University of Exeter, but will be based at the company premises in Mousehole, Cornwall. They will work closely with both the academic team and Chelonia Ltd to develop novel statistical / machine learning techniques for counting and identifying marine mammals from passive acoustic monitoring data. The academic team are based at the Penryn Campus of the University of Exeter, which is a world leading centre for ecology and conservation and close to the Chelonia premises in Mousehole. This is a unique opportunity to launch or develop a highly skilled career in Cornwall, an area of the country which offers an exceptionally high quality of life.

You should:

* have a mathematics or statistics background, with a PhD or nearing completion;
* be able to demonstrate a high level of proficiency in applied spatial or ecological modelling and programming proficiency in technical computing languages such as R or Python, and/or C/C++;
* experience with computational statistics, machine learning and data analysis will be an advantage and the successful applicant will have a strong interest in this area;
* possess excellent time management and communication skills (both written and oral);
* be self-motivated and able to work both independently and collaboratively with the company and academic teams;
* a demonstrable interest in nature conservation, particularly cetaceans, will also be an advantage.