PhD Studentship: Deployable Computer Vision

Although AI is making remarkable progress, its widespread accessibility remains a challenge. High computational costs, significant energy demands, and the lack of efficient, explainable models limit its adoption by the broader public and industries. This is where deployable AI can bridge the gap, making AI more affordable, sustainable, and practical for real-world use.

If you're eager to work on cutting-edge topics such as:

1. Data-efficient learning
2. Explainable AI
3. Memory-efficient deep learning
4. Energy-efficient deep learning
5. Parameter-efficient fine-tuning of foundational models

The PhD studentship will broadly explore cutting-edge methods in deployable AI, focusing on areas such as data-efficient learning, explainable AI, and energy-efficient deep learning. The specific research direction will be fine-tuned in consultation with the supervisor to align with your interests and expertise, ensuring a tailored and impactful research journey.

Interested candidates should send their CV and a brief description of:

1. Your research interests
2. Any relevant past research or project experience 

Supervisor: Dr Shreyank Narayana Gowda

Email me at shreyank.narayanagowda@nottingham.ac.uk to express your interest. After an informal discussion, the application will be sent internally. Don't miss this chance to contribute to impactful research in deployable AI while working in a vibrant academic environment at one of UK's research-intensive Russell Group Universities.

Entry requirements: 

Applicants are normally expected to have a 2:1 Bachelor or Masters degree or international equivalent, in a related discipline. Any offer will be subject to the University admissions requirements. An IELTS score of 6.5 (with 6.0 in each element) or another English Language qualification is also required for candidates who do not have English as a first language. Expected start date 1^st October 2025. 

You will need to meet the minimum entry requirements for our PhD programme. The candidate should be highly motivated and can engage in collaboration with good oral and written communication skills. Previous research experience in machine learning, deep learning and/or computer vision is essential. 

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