|Salary:||£33,519 to £38,833 per annum|
|Placed On:||10th August 2018|
|Closes:||7th September 2018|
Fixed-Term - 2 years.
You will join an offshore renewables research group (www.aurawindenergy.com) and work in collaboration with UK Government (https://ore.catapult.org.uk) and global leaders industry (http://greenporthull.co.uk, https://orsted.co.uk/). You will work as part of an interdisciplinary team in the Energy and Environment Institute, University of Hull, under the direction of Dr Robert Dorrell.
The aim of the project is to develop efficient and reliable sea-state forecasting, to reduce operation and maintenance costs of offshore infrastructure. The project will develop and integrate multiple data sources of sea-state (wave height and period), including met-ocean forecasting, wave buoy, wave radar, optical and SAR satellite imagery.
You will work as part of an international team from academia and industry and collaborate with Prof. Hajime Naruse (University of Kyoto) to develop artificial neural networks for machine learning of statistical prediction of the local-sea state at sub-windfarm to individual turbine scale.
You will lead the machine learning component of the project, and interface with government and industry partners focusing on data acquisition and data mining. You will contribute to project meetings, attend conferences, and be encouraged to produce high-impact publications.
You will have a PhD in a computational sciences, earth sciences, engineering, mathematics, or a related discipline, and will have an established or developing research profile, including publications and appropriate project management experience.
Experience of machine learning would be a distinct advantage. Additionally, experience of water waves and/or forecasting would be advantageous.
To discuss this role informally, please contact the Energy and Environment Institute Executive Officer Jo Dewey: 01482 465343, firstname.lastname@example.org.
Interviews will take place w/c 17th September 2018.
The University of Hull is committed to ensuring equality of opportunity in every aspect of our recruitment processes.
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