Graduate Teaching Assistant (GTA) - Investigation of semi-supervised solutions in cloud detection from remote sensing

University of Reading
Reading
3 weeks ago
Create job alert

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 physicalproperties.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.Theimagery 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 inlearning/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.

  1. Investigate semi-supervised learning mechanisms in dealing with multi-spectralimages.

  2. From the computer vision point of view, analyse remotely sensed images with respecttotheir physical characteristics against image features, which correspond to thick cloud, light cloud, and cloud-free regions.

  3. Develop algorithms, combined with findings from computer vision and machine learning, for the cloud detection purpose with effective but limited training samples.

You will need to demonstrate you:

  • meet the academic requirements for a PhD offer from the University of Reading.
  • have a good (1st or 2.1) first degree in Computer Science, Statistics, Data Science, Mathematical Science, Meteorology, Physics or closely related subjects
  • are able to effectively organise your time and prioritise tasks to balance PhD studies with GTA responsibilities
  • are able to demonstrate scholarship in developing a publication record in your area of specialist expertise and conduct high quality PhD research.
  • are able to communicate scientific concepts clearly and with enthusiasm and in a way that engages students
  • Have good interpersonal skills and be able to work as part of a team


See candidate pack at the bottom of the page for further details.

Candidates will be provided with training to develop teaching and pedagogical skills,no prior experience of teaching is necessary. On the research side, our package of support includes access to MSc courses and bespoke training through ourPostgraduate and Researcher Collegewill help you in developing your professional skills as a researcher.

Working hours for the teaching portion will be variable during the academic year but will be no more than 20 hours per week. The terms of the offer of funding for the PhD and the offer of employment will rely upon the postholder being registered as a full-time doctoral student.

Successful candidates will be paid an annual salary (£8745) and stipend (£15585 per annum) over the 4 year period and will have PhD fees waived at the Home level (Please note that students liable for international fees will need to pay the difference between these and the home fee rate).Fees for 2025/26 (amount payable each year) can be foundhere.

How do I apply?

You must upload a combined CV and Proposal in pdf format(max size 1 MB)and complete the supporting statement.

Closing date: 04/042025

Interview: week commencing 21/04/2025

We look forward to hearing from you!

Contact details for advert

Contact NameDr Hong Wei

Contact Job TitleAssociate Professor in Computer Science

Contact Email address


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 theUK Visas and Immigration Website.

The University is committed to having a diverse and inclusive workforce, supports the gender equality Athena SWAN Charter and the Race Equality Charter, and champions LGBT+ equality. Applications for job-share, part-time and flexible working arrangements are welcomed and will be considered in line with business needs.

Related Jobs

View all jobs

Research Associate - Science of Science

Graduate

Graduate Data Scientist / Engineer – Guildford area £30k- £40k

Graduate Data Analyst

Graduate Claims Analyst

Graduate- HR Data Analyst (m/f/d)

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Top 10 Books to Advance Your Machine Learning Career in the UK

Machine learning (ML) remains one of the fastest-growing fields within technology, reshaping industries across the UK from finance and healthcare to e-commerce, telecommunications, and beyond. With increasing demand for ML specialists, job seekers who continually update their knowledge and skills hold a significant advantage. In this article, we've curated ten essential books every machine learning professional or aspiring ML engineer in the UK should read. Covering foundational theory, practical implementations, advanced techniques, and industry trends, these resources will equip you to excel in your machine learning career.

Navigating Machine Learning Career Fairs Like a Pro: Preparing Your Pitch, Questions to Ask, and Follow-Up Strategies to Stand Out

Machine learning (ML) has swiftly become one of the most in-demand skill areas across industries, with companies leveraging predictive models and data-driven insights to solve challenges in healthcare, finance, retail, manufacturing, and beyond. Whether you’re an early-career data scientist aiming to break into ML, a seasoned engineer branching into deep learning, or a product manager exploring AI-driven solutions, machine learning career fairs offer a powerful route to connect with prospective employers face-to-face. Attending these events can help you: Network with hiring managers and technical leads who make direct recruitment decisions. Gain insider insights on the latest ML trends and tools. Learn about emerging job roles and new industry verticals adopting machine learning. Showcase your interpersonal and communication skills, both of which are increasingly important in collaborative AI/ML environments. However, with many applicants vying for attention in a bustling hall, standing out isn’t always easy. In this detailed guide, we’ll walk you through how to prepare meticulously, pitch yourself confidently, ask relevant questions, and follow up effectively to land the machine learning opportunity that aligns with your ambitions.

Common Pitfalls Machine Learning Job Seekers Face and How to Avoid Them

Machine learning has emerged as one of the most sought-after fields in technology, with companies across industries—from retail and healthcare to finance and manufacturing—embracing data-driven solutions at an unprecedented pace. In the UK, the demand for skilled ML professionals continues to soar, and opportunities in this domain are abundant. Yet, amid this growing market, competition for machine learning jobs can be fierce. Prospective employers set a high bar: they seek candidates with not just theoretical understanding, but also strong practical skills, business sense, and an aptitude for effective communication. Whether you’re a recent graduate, a data scientist transitioning into machine learning, or a seasoned developer pivoting your career, it’s essential to avoid common mistakes that may hinder your prospects. This blog post explores the pitfalls frequently encountered by machine learning job seekers, and offers actionable guidance on how to steer clear of them. If you’re looking for roles in this thriving sector, don’t forget to check out Machine Learning Jobs for the latest vacancies across the UK. In this article, we’ll break down these pitfalls to help you refine your approach in applications, interviews, and career development. By taking on board these insights, you can significantly enhance your employability, stand out from the competition, and secure a rewarding position in the world of machine learning.