[MARMAM] Job opportunity Mathematics / Statistics / Cetacean acoustics
nick.tregenza at chelonia.co.uk
Thu Apr 4 07:19:22 PDT 2019
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
<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
Full details about the position, and a link to apply online can be found
at the link
For informal enquiries, please contact: Dr TJ McKinley
(T.McKinley at exeter.ac.uk
<mailto:T.McKinley at exeter.ac.uk><mailto:T.McKinley at exeter.ac.uk>
<mailto:T.McKinley at exeter.ac.uk>; 01326 259331) at the University of
Exeter, Dr Nick Tregenza (nick.tregenza at chelonia.co.uk
<mailto:nick.tregenza at chelonia.co.uk><mailto:nick.tregenza at chelonia.co.uk>
<mailto:nick.tregenza at 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.
* have a mathematics or statistics background, with a PhD or nearing
* 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.
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