Partager
Institution: University of Nottingham
Royaume-Uni
Retrouvé: : 2024-03-21
Description :

Applications are invited for the above post within the Faculty of Engineering, University of Nottingham, to work on a DEZNS-funded project titled 'Ground-Based Array Network with AI for Enhanced Solar Forecasting and Decarbonised Grid Management'. The primary aim of this project is to develop weather sensors and an artificial intelligence model for forecasting local solar radiation to achieve accurate PV output performance.

The main responsibility of this role is to support the project team in weather array maintenance and weather data platform management. The successful candidate will need to demonstrate that they meet the Role Profile Criteria both in their application and during the interview.

Applicants should demonstrate relevant experience in common data transmission interface protocols and data management. Masters in solar energy engineering or computer science, or mathematics or image processing or a related quantitative field is essential.

Essential requirements include programming skills in Python and familiarity with data science and data visualization libraries. Any experience in developing neural network, regression, and computer visualization models should be highlighted in your application.

Additionally, you will be expected to disseminate your research widely, support other post-graduate researchers, and develop new research collaborations and proposals.

This is a fixed term, part time (10 hours per week) role available for 4 months. 

Requests for secondment from internal candidates may be considered on the basis that prior agreement has been sought from both your current line manager and the manager of your substantive post, if you are already undertaking a secondment role.

Informal enquiries may be addressed to Prof Yupeng Wu, tel: 0115 7484011 Or email Yupeng.Wu@nottingham.ac.uk. Please note that applications sent directly to this email address will not be accepted.

Closing Date: 03 Apr 2024
Category: Research and Teaching (R&T)





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