Associate Research Fellow

3 weeks ago


York North Yorkshire, United Kingdom University of York Full time

The School of Physics, Engineering and Technology brings physicists and engineers together to push the frontiers of knowledge, foster innovation and meet the grand challenges facing society. Our aim is to deliver world-leading research in both fundamental and applied areas whilst developing new technologies that work for the public good, in an environment where everyone can thrive.
As a School, equality, diversity, and inclusion are central to our culture and we strive to provide a working environment which allows all staff and students to contribute fully, to flourish, and to excel. We aim to ensure that there is a supportive and egalitarian culture at all levels and across all staff groups and offer a range of family friendly, inclusive employment policies, flexible working arrangements, staff engagement forums, campus facilities and services to support staff from different backgrounds. We aim to inspire young people to engage with science and engineering through our outreach work.
This role is centered on processing extensive datasets of electroluminescence (EL) imaging for solar photovoltaic (PV) systems and developing convolutional neural network (CNN) algorithms tailored to identify cracks within the EL images. The selected candidate will be a key player in the Aerial Electroluminescence Inspections (AELI) project, which benefits from funding through the UK-Taiwan Collaborative R&D initiative, under the aegis of Innovate UK. First degree in Engineering, Business Analytics, Computer Science, or a related field
PhD in First degree in Engineering, Business Analytics, Computer Science, or a related field, or industrial experience in solar photovoltaics (PV)
Knowledge in CNN networks to engage in high quality research
Knowledge of AI algorithms, evidence evaluation, training, validation, and testing
Ability to write up research work for publication in high profile journals
Skills in building AI algorithms, data analysis and HPC data processing
Experience of carrying out EL testing for PV systems
Dhimish@york.ac.uk or PET HR on pet-hr@york.ac.uk
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