Baihua Li

[Tax-free stipend of £20,780 per annum (in 2025/26) and tuition fees at the UK rate for 3.5 years; Research Training Support Grant (RTSG) of £8,000; International candidates may receive the difference between UK and International tuition fees.] This PhD studentship at Loughborough University offers an exciting opportunity to advance the field of marine biodiversity monitoring through embedded AI and computer vision technologies. In partnership with CEFAS, a global leader in marine science, the project aims to develop scalable, low-cost edge-AI systems for real-time analysis of marine environments. The research will focus on creating robust algorithms capable of classifying diverse marine species and detecting anthropogenic debris under challenging underwater conditions. Building on the Neural Network Enhanced Marine Observation system—a working prototype featuring a shallow-water, edge-AI-enabled spatial camera—the successful candidate will extend its capabilities using novel deep learning and computer vision methods. The project addresses the limitations of current cloud-based solutions by enabling real-time, embedded analysis, which is crucial for efficient and accurate monitoring of nearshore vegetation, shellfish stocks, and epibenthic biodiversity. Supervision will be provided by Professor Qinggang Meng (primary), Professor Baihua Li, and experts from CEFAS, including Jon Hawes and Peter Kohler. The studentship is partially funded by NERC and includes a tax-free stipend of £20,780 per annum (2025/26), UK tuition fees for 3.5 years, and a Research Training Support Grant of £8,000. International candidates are eligible, with additional support for tuition fee differences, though UKRI rules limit international awards to 30% of funded studentships. Applicants should hold, or expect to obtain, at least a 2:1 degree (or equivalent) in Computer Science, Engineering, or a related discipline, with strong programming skills. English language requirements must be met as specified by the university. The application process involves completing the CENTA studentship application form, applying online via the university portal, and quoting reference 'CENTA2026-LU07'. The deadline for applications is January 7th, 2026, with interviews for shortlisted candidates expected in early February 2026. This position is ideal for candidates interested in interdisciplinary research at the intersection of AI, computer vision, and marine science, with significant real-world impact on environmental monitoring and conservation.

[Tax-free stipend of £20,780 per annum (in 2025/26) and tuition fees at the UK rate for 3.5 years. Research Training Support Grant (RTSG) of £8,000. International candidates may have the difference between UK and International tuition fees covered by the University, subject to UKRI funding rules (no more than 30% of studentships to international candidates).] This PhD studentship at Loughborough University offers an exciting opportunity to advance the field of real-time marine biodiversity monitoring using cutting-edge computer vision and edge-AI technologies. In partnership with CEFAS, a global leader in marine science, the project aims to develop scalable, low-cost embedded vision systems capable of analyzing marine biodiversity and detecting anthropogenic debris in challenging underwater environments. The research will focus on designing robust, real-time edge-AI algorithms for accurate classification of diverse marine species and debris, addressing the limitations of current cloud-based computer vision solutions that require specialized expertise and infrastructure. Building on the Neural Network Enhanced Marine Observation system—a working prototype of a low-cost, shallow-water, edge-AI-enabled spatial camera—the project seeks to extend its capabilities for benthic debris classification and comprehensive biodiversity monitoring. The successful candidate will work closely with a multidisciplinary supervisory team, including Professor Qinggang Meng (primary supervisor), Professor Baihua Li, and experts from CEFAS (Jon Hawes and Peter Kohler). The studentship is partially funded by NERC and provides a tax-free stipend of £20,780 per annum (2025/26), UK tuition fees for 3.5 years, and a Research Training Support Grant of £8,000. International candidates are eligible, with the university covering the difference between UK and international tuition fees for successful applicants, subject to UKRI funding rules. Applicants should have at least a 2:1 degree (or equivalent) in Computer Science, Engineering, or a related field, with strong programming skills, and must meet the university's English language requirements. The application process involves submitting a CENTA studentship application form, CV, and supporting documents via the university's online portal, quoting reference 'CENTA2026-LU07'. The deadline for applications is January 7th, 2026, with interviews expected in early February. This project is ideal for candidates passionate about AI, computer vision, and environmental monitoring, offering the chance to contribute to impactful research in marine science.

This PhD project at Loughborough University, supervised by Professor Baihua Li and Huili Chen, aims to develop advanced AI-powered methods for property risk assessment in the context of climate change adaptation and disaster resilience. The research addresses key challenges such as fragmented data, uncertainty, and passive data use by investigating uncertainty-aware multimodal data fusion and decision-theoretic data acquisition strategies. By integrating distributed digital data sources with satellite observations and leveraging computer vision techniques, the project enables adaptive and dynamically updateable risk assessment models. The project is co-designed with industry partners to ensure practical relevance and supports improved decision-making for insurers and policymakers. A predictive case study will demonstrate the value of these methods in real-world scenarios. The research is part of the Informatics for Multi-hazard Risk and Resilience (i-Risk) NERC Doctoral Focal Awards (DFA) in Environmental Sciences, offering a unique opportunity to contribute to climate change adaptation and disaster resilience. Funding is provided through the UKRI i-Risk Doctoral Focal Award, covering a tax-free stipend of £21,805 per annum for 3.5 years and tuition fees at the UK rate. Excellent international candidates may be eligible for a full international fee waiver, subject to funding quotas. No bench fees are required. The position is available full-time (3.5 years) or part-time (7 years), and both UK and international applicants are welcome. Applicants must hold, or expect to soon graduate with, a very good undergraduate or Master’s degree (at least UK 2:1 honours or equivalent international qualification) in a relevant subject. EU and Overseas applicants must achieve an IELTS score of 6.5 with at least 6.0 in each competency. Minimum English language requirements apply, and further details are available on the university’s international website. To apply, candidates should submit an online application via the Loughborough University portal, selecting 'School of Science' as the programme name and quoting reference IRISK-26-LU-08. Required documents include a two-page personal statement (covering research interests and responses to specific questions), CV, academic transcripts and degree certificates (translated if not in English), and IELTS/TOEFL certificate if applicable. Applicants are encouraged to contact supervisors by email to discuss project-specific aspects prior to submitting their application. Interviews are anticipated to be held remotely via Microsoft Teams during the week commencing 29 June 2026. For further information about the i-Risk DFA, visit the project website. For general enquiries, contact [email protected] or [email protected].

PhD studentship at Loughborough University in AI-powered uncertainty-aware adaptive methods for property risk assessment , linked to climate change adaptation and disaster resilience . The project develops novel AI , machine learning , and computer vision methods for property risk assessment under climate- and disaster-related hazards. It focuses on uncertainty-aware multimodal data fusion, decision-theoretic data acquisition, and adaptive risk estimation using distributed digital data sources and satellite observations. Study area keywords: Computer Science, Environmental Science, Civil Engineering, Architecture, Statistics. Funding: Fully funded PhD studentship for UK and international applicants, funded by NERC and the i-Risk CDT . Full-time duration is 3.5 years. Eligibility: Applicants should have or expect to shortly graduate with at least a UK 2:1 honours degree or equivalent international qualification in a relevant subject. EU and overseas applicants should normally meet IELTS 6.5 overall with 6.0 in each component, unless a language-test waiver or equivalent evidence applies. Supervisors: Primary supervisor Baihua Li; secondary supervisor Huili Chen. Deadline: 9 June 2026. Start date: October 2026. Application: Apply online via the Loughborough University project page and upload the required personal statement, CV, transcripts, and degree certificates.

Three fully funded PhD opportunities are available at Loughborough University through the UNESCO Chair in Informatics and Multi-hazard Risk Reduction (UNESCO-IMRR) . These projects sit within informatics, disaster risk reduction, climate adaptation, and resilience-focused engineering research. 1) Digital Twin-enabled Monitoring and Assessment of Hydro-environmental Risks in Heritage Masonry Buildings Supervisors: Dr Zhiqi Hu , Prof Qiuhua Liang This PhD develops a digital twin framework to assess rainfall, groundwater fluctuation, moisture-related processes, deformation, deterioration, and vulnerability in heritage masonry buildings. It combines point cloud and image data, building information models, and environmental exposure data to support inspection planning, maintenance prioritisation, and resilience-informed decision-making. 2) Data-driven Optimisation of Hydraulic Model Calibration and Flood Risk Interventions for Enhanced Resilience and Investment Efficiency Supervisors: Prof Qiuhua Liang , Dr Huili Chen , Dr Huili Fang This project focuses on hydraulic modelling, flood risk, and optimisation of interventions to improve resilience and investment efficiency. It is aligned with data-driven methods for calibration and decision support in flood risk management. 3) AI-Powered Uncertainty-Aware Adaptive Methods for Property Risk Assessment in Climate Change Adaptation and Disaster Resilience Supervisors: Prof Baihua Li , Dr Huili Chen This PhD develops novel AI methods for property risk assessment under climate and disaster-related hazards. It emphasises uncertainty-aware multimodal data fusion, decision-theoretic data acquisition, satellite observations, and computer vision to enable adaptive and dynamically updateable risk assessment. Funding: All three PhDs are fully funded for UK and international students. The projects are full-time for 3.5 years (or part-time for 7 years). Eligibility: Applicants should have or expect to shortly graduate with at least a UK 2:1 honours degree or equivalent in a relevant subject. International applicants should meet the English language requirement (IELTS 6.5 overall with 6.0 in each component, or TOEFL if applicable). Application window: Deadline is 9 June 2026 . Applicants must submit a CV, certified transcripts and degree certificates, and a two-page personal statement tailored to the project and i-Risk DFA.