Research Fellow in Ocean Renewable Energy Modelling – Bangor University

Research Fellow in Ocean Renewable Energy Modelling – Bangor University

Applications are invited for a fixed-term (41 months), full-time research fellow to develop models of ocean renewable energy resource characterization, working in the School of Ocean Sciences at Bangor University, on the SEEC project.

The scheme has been part-funded by the European Regional Development Fund through the Welsh Government. EU Funds:
Investing in Wales.

Duties will include:

• Development and validation of a suite of ocean models applied to ocean renewable energy resource assessment and characterization over a range of spatial and temporal scales.
• Integrating the developed ocean models with in situ and remote sensing data.
• Development of research ideas relating to ocean renewable energy.
• Leading the design and publication of scientific outputs in the peer-reviewed literature.
• Leading the development of external research grant applications.
• Attending and possibly convening sessions at national and international meetings, and developing and presenting scientific outputs.
• You will be expected to work closely with colleagues investigating energy resources in related research fields.
• Leading on the organisation and supervision of project teams.

Candidates should be educated to PhD standard (or equivalent) in an area of ocean modelling, applied mathematics, physics, computing or engineering and have previous experience of developing computer models and performing ocean renewable energy research.

The successful candidate will be expected to commence 1 November 2019 or as soon as possible thereafter and the post is available until 30/03/2023.

Applications will only be accepted via our on-line recruitment website,  However, in cases of access issues due to disability, paper application forms are available by telephoning

Informal enquiries can be made by contacting Dr Simon Neill (SEEC Director), email

The College of Environmental Sciences and Engineering at Bangor University consists of three academic schools: the School of Computer Science and Electronic Engineering, the School of Natural Sciences, and the School of Ocean Sciences. The college has a wide range of research and teaching facilities including: large, modern, well equipped teaching and research laboratories; a £3.5 million seagoing research vessel and three smaller inshore vessels;

a Natural History Museum with extensive invertebrate and vertebrate collections; a research farm; the Bio Composites Centre;
Treborth Botanic Garden;
Environment Centre for Wales, and extensive marine and freshwater aquaria.

The School of Ocean Sciences (SOS) is one of the largest university marine science departments in Europe. It is a multi-disciplinary department located on the shores of the Menai Strait, North Wales, UK. SOS has a rich history and international reputation for measuring and simulating oceanographic conditions, with expertise on turbulence, waves and tidal flows, including the application of 3D dynamically coupled wave-tide modelling.

SOS has excellent new research laboratories (Marine Centre Wales), access to high performance computing facilities, and runs its own shelf sea research vessel (the 35 m RV Prince Madog), which is fully supported by a range of seagoing equipment (e.g. multibeam, wave buoys and acoustic Doppler current profilers) and a dedicated team of seagoing technicians. SOS has strong links with the ocean renewable energy industry through several structural fund and UK research council projects.

The Project

The Smart Efficient Energy Centre (SEEC) is a £7M European Regional Development Fund (ERDF) project administered through the Welsh European Funding Office (WEFO). The key objective of SEEC is to increase the success of Bangor University in attracting competitive and private research funding in low carbon energy research, thereby enhancing specialist research capacity.

SEEC is an interdisciplinary data, modelling, and knowledge resource that utilises a new cyberinfrastructure to develop new smart sensor networks, data design and control, and diagnostic/prognostic models, to reduce engineering uncertainties and increase resource efficiencies in low carbon…