PhD Studentship in Deployable, Efficient, and Trustworthy Computer Vision

University of Nottingham; Nottingham; On-site; GBP 21,000; Full time

Job summary

A leading research university in the UK is inviting applications for a fully funded PhD studentship focused on deployable, efficient, and trustworthy computer vision. The successful candidate will join an innovative research group, developing novel algorithms for AI systems designed to operate on edge devices. Ideal candidates will possess a strong academic background in relevant fields and programming skills in Python and AI frameworks. The studentship provides full tuition and a competitive tax-free stipend over 3.5 years.

Benefits

Fully funded tuition fees

Competitive tax-free stipend

Access to high-performance computing resources

Qualifications

• Enthusiastic, curious, and motivated individuals are sought.
• Prior research experience is desirable but not essential.

Responsibilities

• Develop resource-efficient computer vision methods.
• Explore novel algorithms for edge devices and low-power hardware.
• Shape research direction based on interests.

Skills

Computer science

Artificial intelligence

Machine learning

Data science

Programming skills

Education

Strong academic background in a related discipline

Tools

Python

PyTorch

TensorFlow

Job description

The School of Computer Science at the University of Nottingham is pleased to invite applications for a fully funded PhD studentship in deployable, efficient, and trustworthy computer vision. This is an exciting opportunity to join a leading research group within a Russell Group university, working at the forefront of artificial intelligence research with real-world impact. The studentship covers full tuition fees for UK (home) students and provides a competitive tax-free stipend for three and half years.

Project Overview

Modern AI systems are powerful but often expensive, memory-hungry, and difficult to deploy outside large data centres. This PhD project focuses on developing resource-efficient computer vision methods that maintain strong performance while dramatically reducing computation, memory, and energy requirements. The successful candidate will explore novel algorithms and model-design strategies that allow AI systems to operate effectively on edge devices, clinical environments, or low-power hardware.

Key research directions include (but are not limited to):

• Data-efficient learning: Zero-shot and few-shot recognition, semi-supervised learning, and techniques that reduce reliance on large annotated datasets.
• Memory-efficient deep learning: Model compression, pruning, quantisation, selective memory replay, and efficient training strategies.
• Energy-efficient deep learning: Methods that lower the carbon and computational footprint of training and inference.
• Parameter-efficient fine-tuning: Harnessing large foundational vision–language models using adapters, LoRA, low-rank updates, and other lightweight personalisation techniques.

Candidates will have freedom to shape the exact direction of the research depending on their interests and background. You will work under the supervision of Dr Shreyank NGowda within a supportive and vibrant research environment that encourages collaboration, publication in top-tier venues, and engagement with the wider AI community.

Candidate Requirements

We are seeking enthusiastic, curious, and motivated individuals with:

• A strong academic background in computer science, artificial intelligence, machine learning, data science, engineering, or a related discipline.
• Good programming skills (e.g., Python, PyTorch, TensorFlow).
• A genuine interest in pushing the boundaries of efficient and deployable AI.

Prior research experience (e.g., project work, MSc dissertation, publications, internships) is desirable but not essential.

Training and Environment

The University of Nottingham offers an excellent research training environment, access to high-performance computing resources, opportunities for interdisciplinary collaboration, and professional development support. Students will be encouraged to publish their work in leading journals and conferences and to participate in workshops, doctoral training events, and international research exchanges.

How to Apply

Interested candidates should email [e-mail] with:

• A CV
• A brief statement (1–2 paragraphs) describing:

• Your research interests
• Any relevant past research or project experience

Please include “PhD Studentship Application – Deployable Computer Vision” in the email subject line.

Up to £20,780 per year and tuition fees and fully funded for 3.5 years