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College of Engineering, Mathematics and Physical Sciences
Department of Mathematics
The post is available from 7 January 2019 until 30 March 2021 to work on the NERC funded Campus project
The University of Exeter is a member of the prestigious Russell Group of research-intensive universities. We combine world-class teaching with world-class research, and have achieved a Gold rating in the Teaching Excellence Framework Award 2017. The University of Exeter has over 22,000 students and 4600 staff from 180 different countries and has been rated the WhatUni2017 International Student Choice. Our research focuses on some of the most fundamental issues facing humankind today, with 98% of our research rated as being of international quality in the 2014 Research Excellence Framework.
The project is concerned with monitoring the state of the ecosystem in shelf seas around the UK using a combination of modelling and autonomous vehicles. The shelf sea ecosystem around the UK is under considerable pressure from climate change and pollution. Monitoring the state of the ecosystem using conventional means is prohibitively expensive. CAMPUS is concerned with the development of new methods for monitoring the state of the seas using a combination of numerical modelling and autonomous vehicles (mainly gliders and autonomous underwater vehicles - AUVs). The University of Exeter will undertake two tasks in the CAMPUS programme: the first is to build a statistical spatio-temporal model for short term forecasting; and the second is to further our in-house developed strategies for the guidance and control of autonomous vehicles.
The post will be concerned with the development of statistical spatio-temporal models to produce short term forecasts of the shelf sea ecosystem from a combination of dynamical model output (both physical and biological models) and data collected by ships and intelligently navigated gliders. In addition the post holder will work (with Scottish Association for Marine Science) on the development and implementation of novel guidance and control algorithms for underwater vehicles (gliders and AUVs).
You will:
- Be able to program mathematical statistical models in a high level language (e.g. in R/Python/Matlab) and implement guidance and control schemes in a low level language (e.g., in C++) and Robot Operating System middleware to carry out experiments in the ocean environment
- Be able to demonstrate a good understanding of integrated 'plug and play' systems and knowledge of the underlying communication aspects
- Possess a PhD or equivalent in a related field of study (Control Engineering, Engineering mathematics, Machine Learning and Robotics, Mechatronics, Statistical learning or a related discipline to project)
- Be able to demonstrate sufficient knowledge in the discipline to work within established research programmes
- Be able to present information on research progress and outcomes, communicate complex information, orally, in writing and electronically
To view the Job Description and Person Specification document please click here.
For further information please contact Dr Prathyush Menon P.M.Prathyush@exeter.ac.uk or 01392 72649.
The University offers some fantastic benefits including 41 days leave per year, options for flexible working, numerous discounts at leading retailers, onsite gym, cycle to work scheme, sector leading policies around maternity, adoption and shared parental leave (up to 26 weeks full pay), paternity leave (up to 6 weeks full pay), and a new Fertility Treatment Policy.
The department is proud to have a Silver Athena SWAN award in recognition of their commitment to providing equality of opportunity and advancing the representation of women in STEM/M subjects. All of the University of Exeter's STEM/M departments hold an Athena SWAN award.
The University of Exeter is an equal opportunity employer. We are officially recognised as a Disability Confident employer and an Athena Swan accredited institution. Whilst all applicants will be judged on merit alone, we particularly welcome applications from groups currently underrepresented in the workforce.