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The University of Reading offers a Graduate Teaching Assistant position in Computer Science focused on developing semi-supervised solutions for cloud detection via remote sensing. Candidates will engage in a funded PhD program while gaining teaching experience, offering a unique opportunity to advance their academic and professional careers.
Join to apply for the Graduate Teaching Assistant (GTA) - Investigation of semi-supervised solutions in cloud detection from remote sensing role at University of Reading
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Join to apply for the Graduate Teaching Assistant (GTA) - Investigation of semi-supervised solutions in cloud detection from remote sensing role at University of Reading
Applications from job seekers who require sponsorship to work in the UK are welcome and will be considered alongside all other applications. However, non-UK candidates who do not already have permission to work in the UK should note that by reference to the applicable SOC code for this role, sponsorship will not be possible under the Skilled Worker Route. There is further information about this on the .
Part-time, fixed-term (4 year) role.
Closing date: 23:59 GMT 20 July 2025
Interview date: to be confirmed
We are pleased to announce a fantastic opportunity for ambitious computer scientists to join our Computer Science Graduate Teaching Assistant (GTA) Programme!
How does it work?
Candidates will study for a four year, full time funded PhD (3 quarters of your time) whilst working and receiving a salary to gain valuable teaching experience (1 quarter of your time). Candidates will receive a salary and stipend package that exceeds the standard UKRI stipend for a full-time PhD.
Home/RoI Students will have their PhD fees waived, International students will receive a fee waiver equivalent to the Home/RoI fee and will be expected to fund the difference between the International fee and the Home/RoI fee. There will be a package of support to enable you to develop a research career in this exciting field.
PhD Topic: Investigation of semi-supervised solutions in cloud detection from remote sensing.
Earth observation satellite imagery provides earth surface appearance as well as physical properties. However, clouds over the surface (including oceans) negatively affect the observation of optical images. To understand true surface properties, cloud detect is required to identify which pixels within an image are dominated by cloud-free regions, as opposed to cloud dominated pixels. The imagery involved is usually in multiple spectral captured by satellites. The project aims to design and develop algorithms to automatically detect cloud dominated pixels from given satellite images with limited annotated samples in learning/training.
The project aims to apply computer vision and machine learning technologies in cloud detection from remotely sensed imagery by using limited training samples. With respect to the aim, the following objectives are specified.
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