Science And Engineering: Fully Funded PhD Scholarship: Forecasting Future Environmental Impacts of Photovoltaic (PV) Manufacture Using Machine Learning Techniques (RS573)

  • DeadlineDeadline: 27 March 2024
  • WalesWales

Description

Closing date: 27 March 2024

Funding providers: Swansea University’s Faculty of Science and Engineering, and STRIP5 Prosperity Partnership

Subject areas: Environmental Science, Engineering, Mathsthe Physical Sciences

Project start date:

  • July 2024 (Enrolment open from mid-June)
  • 1 October 2024 (Enrolment open from mid-September)

Supervisors:

Aligned programme of study: PhD in Mechanical Engineering

Mode of study: Full-time or Part-time study is possible.

Project description:

This PhD will suit applicants who are keen to make a difference in how we utilise advanced materials in a sustainable economy. Applicants with undergraduate degrees in Environmental Science, Engineering, Maths or the Physical Sciences will be suited to the position. The research focus can be adjusted depending on the experience of the successful candidate.

This project tackles the challenge of predicting environmental impacts in solar panel manufacturing where the number of data sets available for some of the materials and processes are limited. This is critical to accelerate progress to lower emission manufacturing of renewable technologies.

The focus of the project will be to leverage innovative machine learning (ML) techniques adept at handling sparse information. The impact of data pre-processing techniques will be assessed with the aim of improving the accuracy of predictions. Databases of known materials will be used for validation.

The key objective is to develop a predictive model for cradle to grave environmental impacts of third generation PV which can adjust for different manufacturing location, policy decisions, economic situations etc. This is an ambitious target, and the objective will be split up into separate device components (eg substrate / electrode / active material) and lifecycle stages to enable the student to succeed in their studies even if they are only able to achieve a portion of the model.

This research aims to pioneer adaptable methodologies, providing actionable insights for sustainable PV manufacturing. By creatively leveraging available data and embracing adaptive approaches, the project aims to contribute to sustainable practices within developing manufacturing industries.

For more details please see here: https://www.swansea.ac.uk/postgraduate/scholarships/research/mechanical-engineering-su-strip5-phd-forecasting-2023-rs573.php

Entry Requirements

Candidates must hold an undergraduate degree at 2.1 level in Engineering or similar relevant science discipline. If you are eligible to apply for the scholarship (i.e. a student who is eligible to pay the UK rate of tuition fees) but do not hold a UK degree, you can check our comparison entry requirements (see country specific qualifications). Please note that you may need to provide evidence of your English Language proficiency.

Applicants with undergraduate degrees in Environmental Science, Engineering, Mathsor the Physical Sciences will be suited to the position.  

Due to funding restrictions, this scholarship is open to applicants eligible to pay tuition fees at the UK rate only, as defined by UKCISA regulations. 

If you have any questions regarding your academic or fee eligibility based on the above, please email pgrscholarships@swansea.ac.uk with the web-link to the scholarship(s) you are interested in.

Fees

This scholarship covers the full cost of UK tuition fees and an annual stipend at UKRI rate (currently £18,622 for 2023/24) for four years.

Additional research expenses will also be available.

How To Apply

To apply, please complete your application online with the following information:

1. Course choice – please select Mechanical Engineering / PhD / Full-time (or part-time) / 3 Years (or 6 years) / July or October

In the event you have already applied for the above programme previously, the application system may issue a warning notice and prevent application, in this event, please email pgrscholarships@swansea.ac.uk where staff will be happy to assist you in submitting your application.

2. Start year – please select 2024
3. Funding (page 8) –

  • ‘Are you funding your studies yourself?’ – please select No
  • ‘Name of Individual or organisation providing funds for study’ – please enter ‘RS573 – Machine Learning Techniques’

*It is the responsibility of the applicant to list the above information accurately when applying, please note that applications received without the above information listed will not be considered for the scholarship award.

One application is required per individual Swansea University led research scholarship award; applications cannot be considered listing multiple Swansea University led research scholarship awards.

We encourage you to complete the following to support our commitment to providing an environment free of discrimination and celebrating diversity at Swansea University:

As part of your online application, you MUST upload the following documents (please do not send these via email). We strongly advise you to provide the listed supporting documents by the advertised closing date, where possible:

  • CV
  • Degree certificates and transcripts (if you are currently studying for a degree, screenshots of your grades to date are sufficient)
  • A cover letter including a ‘Supplementary Personal Statement’ to explain why the position particularly matches your skills and experience and how you choose to develop the project.
  • Two references (academic or previous employer) on headed paper or using the Swansea University reference form. Please note that we are not able to accept references received citing private email accounts, e.g. Hotmail. Referees should cite their employment email address for verification of reference.
  • Evidence of meeting English Language requirement (if applicable).
  • Copy of UK resident visa (if applicable)
  • Confirmation of EDI form submission (optional) 

Informal enquiries are welcome, please contact Dr Jenny Baker (J.Baker@Swansea.ac.uk).

*External Partner Application Data Sharing – Please note that as part of the scholarship application selection process, application data sharing may occur with external partners outside of the University, when joint/co- funding of a scholarship project is applicable.

Who is eligible to apply?

Due to funding restrictions, this scholarship is open to applicants eligible to pay tuition fees at the UK rate only, as defined by UKCISA regulations. 

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