PhD Studentship: Scene Understanding in Challenging Environments with Multimodal Deep Learning

Accurate 3D scene understanding is critical for robotics, autonomous driving, and similar applications. While current models perform well in standard environments (e.g., good lighting, no occlusions), they struggle in challenging conditions like darkness, fog, rain, and occlusions. This project aims to advance scene understanding in such environments using multimodal deep learning approaches. Leveraging cutting-edge technologies such as large models, diffusion models, and vision transformers, the research will focus on integrating diverse data inputs, including 2D RGB images, thermal images, depth maps, and 3D point clouds.

<p>Applications in robotics, autonomous driving, and similar fields rely heavily on accurate 3D scene understanding. While state-of-the-art models have achieved remarkable performance in standard environments (e.g., with good lighting, no occlusions, and clean image data), understanding 3D scenes in challenging environments (e.g., darkness, occlusions, smoke, fog, rain, or snow) remains a significant challenge. Conventional approaches rely heavily on single-modal data (e.g., 2D RGB images), which limits their effectiveness in non-standard scenarios. Moreover, current models trained on standard conditions often fail to generalize to these difficult environments, leaving room for substantial improvement.</p> <p>This project aims to leverage advanced deep learning technologies—including large models, diffusion models, and vision transformers—to develop novel algorithms for multimodal data integration. The goal is to enhance machine perception and understanding of complex scenes in challenging conditions using diverse data inputs, such as: 2D RGB images, Thermal images, Depth images, 3D point clouds.</p> <p>Please feel free to contact the main supervisor informally with your CV and questions for a discussion.</p> <p> </p>

<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 </strong></em>and in the research information section that the research degree you wish to be considered for is <em><strong>Scene Understanding in Challenging Environments with Multimodal Deep Learning</strong></em> as well as <a href="https://eps.leeds.ac.uk/computing/staff/15544/qian-xie">Dr Qian Xie</a> as your proposed supervisor. <em><strong>Please state clearly in the Finance section that the Funding Source you are applying for is the School of Computer Science Scholarship 2025/26.</strong></em></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 28 February 2025:</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>A highly competitive School of Computer Science Studentship providing the award of full academic fees, together with a tax-free maintenance grant at the standard UKRI rate (£19,237 in academic session 2024/25) for 3.5 years. There are no additional allowances for travel, research expenses, conference attendance or any other costs.</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>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>