PhD Studentship in Deployable, Efficient, and Trustworthy Computer Vision

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 N Gowda 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 shreyank.narayanagowda@nottingham.ac.uk 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.

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