Additional Information: This role is an externally funded fixed term for a total of 37 months, full time.
We are seeking a candidate to work as a Post Doctoral Research Assistant as part of a multidisciplinary team in support of research funded by the EU under project grant B-GOOD. The successful candidate will underpin fundamental research into honey bee health and the automated monitoring of hives.
The vision of B-GOOD is to pave the way towards healthy and sustainable beekeeping within the European Union by following a collaborative and interdisciplinary approach. Merging data from within and around beehives as well as wider socioeconomic conditions, B-GOOD will develop and test innovative tools to perform risk assessments according to the Health Status Index (HSI). B-GOOD has the overall goal to provide guidance for beekeepers and help them make better and more informed decisions.
The role will involve working in close collaboration with, including possibly spending short periods of time at, consortium research organisations in Europe.
This project will use extensive vibrational data sets acquired on a range of different honeybee hives, in different European countries. The research team aims to undertake and optimise machine learning algorithms on the vibrational spectra, in order to devise colony status assessments.
This is an ideal opportunity to make a valuable contribution to impactful research, and to develop high-level skills in the area of machine learning, automated condition monitoring, honeybee behaviour, vibrational measurements and signal processing.
The successful candidate will assist the project lead in writing matlab® and/or GNU Octave code in order to undertake the numerical analysis required. Specific duties will include, in particular, beekeeping (appropriate training will be given where necessary), including driving to and from a UK based apiary, statistical analysis, machine learning, maintenance of colony monitoring hardware, contributing to conference presentations and publications, and liaising with non-academic audiences and stakeholders, etc.
You will have a Ph.D. in science in a relevant field and a good record of academic excellence. You will have good knowledge of core concepts in machine learning and/or data mining, and have experience in conducting research from conception through to dissemination. You will be motivated and ambitious.
The post holder will be organised with excellent communication skills, will hold a UK/EU car driving license, and will be able to work effectively within a small team and the wider consortium. An honours degree in Physics, Computing, Electronics or other closely related discipline is required.
lectronics or other closely related discipline is required.If you have any specific queries in relation to this position please contact Dr Martin Bencsik, Associate Professor via email on email@example.com
Please note this role does not meet the UK Border Agency requirements for sponsorship.
Nottingham Trent University is unable to apply for sponsorship for any applicant not eligible to work in the UK and therefore we cannot progress applications from candidates who require sponsorship under the Points Based Immigration System.
Further application details are available at www.ntu.ac.uk/vacancies. If you require documentation in alternative formats (e.g. Braille, large print) please contact us at firstname.lastname@example.org
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