Research Associate

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 are proud to hold Juno Champion and Athena Swan bronze awards, which recognise our commitment to creating an equitable and fully inclusive environment in which staff and students can thrive. We aim to inspire young people to engage with science and engineering through our outreach work.

Role
This role is centred 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. The role demands a synergy of efforts to pioneer novel CNN algorithms. These algorithms will be specifically designed to automate the detection of cracks in EL images of solar PV panels or systems, thereby streamlining the process of analysing images to estimate the output power losses attributable to the detected solar cell defects. The successful applicant will start on 1 June 2024 or as soon as possible thereafter.

Skills, Experience & Qualification needed

Qualifications

• 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

• Knowledge in CNN networks to engage in high quality research.
• Knowledge of AI algorithms, evidence evaluation, training, validation, and testing.
• Knowledge of EL/PL/Thermal IR PV inspections techniques.

Skills, abilities, and competencies

• Ability to write up research work for publication in high profile journals.
• Competency to make presentations at conferences or exhibit work in other appropriate events.
• Skills in building AI algorithms, data analysis and HPC data processing.

Experience

• Ability to work as part of a team and also to work independently using own initiative.
• Experience of carrying out EL testing for PV systems.
• Experience of developing AI algorithms (ideally deep learning methods such as CNN).

Personal attributes

• Collaborative ethos.
• Positive attitude to colleagues and students.

Interview date: TBC.

For informal enquiries: please contact Dr Mahmoud Dhimish on Mahmoud.Dhimish@york.ac.uk or PET HR on pet-hr@york.ac.uk.

The University strives to be diverse and inclusive – a place where we can ALL be ourselves.

We particularly encourage applications from people who identify as Black, Asian or from a Minority Ethnic background, who are underrepresented at the University.

We offer family friendly, flexible working arrangements, with forums and inclusive facilities to support our staff. #EqualityatYork

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