PhD Studentship: Adapting Large Pretrained Models to Non-Standard Conditions

Modern AI systems, powered by large pretrained models, have demonstrated exceptional performance across a variety of applications, particularly when trained and deployed in standard conditions. However, real-world scenarios are often far from ideal, presenting challenges such as varying lighting, environmental noise, sensor degradation, and domain shifts. The ability to adapt pretrained models for robust operation in such non-standard conditions is becoming increasingly critical, especially in domains like autonomous systems and disaster response.<br /> <br /> Pretrained models are typically developed using datasets that represent standard conditions, which limits their effectiveness in non-standard environments. Direct training for every possible scenario is computationally prohibitive, and the scarcity of labelled data in non-standard conditions exacerbates this issue. These limitations highlight the need for innovative approaches to fine-tune or adapt pretrained models for robust performance in diverse and challenging settings.<br /> <br /> This project aims to develop advanced methodologies to adapt pretrained models for non-standard conditions, focusing on strategies such as fine-tuning with limited or weakly labelled data, leveraging domain adaptation and transfer learning to bridge the gap between standard datasets and real-world scenarios, and enhancing robust feature representations to ensure stability under varying environmental and sensory conditions. Additionally, it seeks to evaluate the potential of multi-task learning frameworks to utilize auxiliary tasks for more effective adaptation, ultimately enabling AI systems to generalize and perform reliably in diverse and challenging real-world applications.

<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>PHD Computer Science FT</strong></em>, in the research information section that the research degree you wish to be considered for is <strong>Adapting Large Pretrained Models to Non-Standard Conditions</strong> as well as <a href="https://eps.leeds.ac.uk/computing/staff/15544/qian-xie">Dr Qian Xie</a> as your proposed supervisor and in the finance section, please state clearly the <strong><em>funding source that you are applying for, if you are self-funding or externally sponsored.</em></strong></p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p> <p>Applications will be considered on an ongoing basis.  Potential applicants are strongly encouraged to contact the supervisors for an informal discussion before making a formal application.  We also advise that you apply at the earliest opportunity as the application and selection process may close early, should we receive a sufficient number of applications or that a suitable candidate is appointed.</p> <p><strong>Please note that you must provide the following documents in support of your application by the closing date of Monday 3 March 2025 if applying for Leeds Doctoral Scholarship or Tuesday 1 April 2025 for Leeds Opportunity Research Scholarship.</strong></p> <ul> <li>Full Transcripts of all degree study or if in final year of study, full transcripts to date</li> <li>Personal Statement outlining your interest in the project</li> <li>CV</li> </ul>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study in the School of Computer Science is an IELTS of 6.5 overall with at least 6.5 in writing and at least 6.0 in reading, listening and speaking or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<p><strong>Self-Funded or externally sponsored students are welcome to apply.</strong></p> <p><strong>UK</strong> – The <a href="https://phd.leeds.ac.uk/funding/138-leeds-doctoral-scholarship-2025-faculty-of-engineering-and-physical-sciences">Leeds Doctoral Scholarship</a> <strong>(closing date: Monday 3 March 2025)</strong> and <a href="https://phd.leeds.ac.uk/funding/234-leeds-opportunity-research-scholarship-2022">Leeds Opportunity Research Scholarship</a> <strong>(closing date: Tuesday 1 April 2025)</strong> are available to UK applicants.  <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary award</a> is available to graduates of the University of Leeds.</p> <p><strong>Non-UK</strong> – <a href="https://phd.leeds.ac.uk/funding/48-china-scholarship-council-university-of-leeds-scholarships-2021">The China Scholarship Council </a>- University of Leeds Scholarship is available to nationals of China <strong>(now closed for October 2025 entry)</strong>. The <a href="https://phd.leeds.ac.uk/funding/73-leeds-marshall-scholarship">Leeds Marshall Scholarship</a> is available to support US citizens. <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary award</a> is available to graduates of the University of Leeds.</p> <p>You will be responsible for paying the overtime fee in full in your writing up/overtime year (£320 in Session 2024/25), but the scholarship maintenance allowance will continue to be paid for up to 6 months in the final year of award.</p> <p>Important: Please note that that the award does not cover the costs associated with moving to the UK.  All such costs (<a href="https://www.leeds.ac.uk/international-visas-immigration/doc/applying-student-visa">visa, Immigration Health Surcharge</a>, flights etc) would have to be met by yourself, or you will need to find an alternative funding source. </p> <p>Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals.</p>

<p>For further information about this project, please contact Dr Qian Xie by email to <a href="mailto:Q.Xie2@leeds.ac.uk">Q.Xie2@leeds.ac.uk</a>.</p> <p>For further information about your application, please contact PGR Admissions by email to <a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a>.</p>