雷丁大学PhD Position in Leveraging AI and Machine Learning to Investigate Shy Shark Abundance in Kelp Forests申请条件要求-申请方
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雷丁大学
PhD直招介绍
About the Project
*Please note that this PhD will be hosted at the University of Reading*
The proposed research will employ artificial intelligence (AI) and machine learning (ML) to monitor sharks (focussing on elusive shy sharks) within the kelp forests of the Western Cape region, South Africa. In collaboration with Cape RADD, a South African-based marine conservation organisation, we aim to develop advanced methodologies that will enhance our understanding of shark diversity and populations within these critical marine ecosystems.
Key Objectives:
Develop AI and ML Models: Create sophisticated AI and ML algorithms capable of analysing underwater imagery and sensor data to identify and count different shark species within the kelp forest.
Data Collection: We plan to gather high-resolution underwater images, videos, and other relevant sensor data from various locations within the kelp forests through collaborations with Cape RADD, citizen scientists and through community engagement.
Biodiversity Assessment: Use AI-driven analysis to identify sharks in the field and estimate shark populations and assess their distribution and behaviour at study sites.
Conservation Insights: Provide insights into the current state of shark populations, identify potential threats, and suggest conservation strategies based on the findings. There is potential to elaborate into a broader range of species in the kelp forest using similar techniques.
Methodology:
Data Acquisition:
Underwater Imagery: Utilise citizen science and open source photography (recreational divers) to capture extensive underwater footage. Deployment of Baited Remote Underwater Video systems (BRUV) at specific sites in collaboration with Cape RADD's field teams.
Receiver Deployment: Deploy acoustic receiver and tags to monitor coastal shark movements. Potential to tag internal R code acoustic transmitters.
AI and ML Model Development:
Image Processing: Train convolutional neural networks (CNNs) to recognise and classify different shark species from the collected imagery.
Pattern Recognition: Develop ML algorithms to analyse movement patterns and estimate population sizes.
Data Integration: Integrate data from various sensors to enhance accuracy and depth of analysis.
Analysis and Interpretation:
Population Estimation: Use AI models to estimate the number of sharks and their spatial distribution within the kelp forests.
Behavioural Insights: Analyse behavioural patterns and interactions with other species and environmental factors.
Training Opportunities:
A comprehensive training programme will be provided, comprising training both in applied AI and biodiversity, and transferable professional and research skills. The project includes a placement with an AI-INTERVENE project partner of between 3-18 months in duration. The student will present at national and international conferences, placing the student at the forefront of the discipline, leading to excellent future employment opportunities.
References
[1] ( https://www.sciencedirect.com/science/article/pii/S1574954122001236 )
雷丁大学 PhD Position in Leveraging AI and Machine Learning to Investigate Shy Shark Abundance in Kelp Forests项目有没有奖学金，是不是全奖Phd招生，下面我们一起看一下【大学名称】Phd的奖学金资助情况
项目资助情况
Subject to a competition to identify the strongest applicants, this studentship would be fully funded by the AI-INTERVENE NERC Doctoral Focal Award.
雷丁大学Phd申请条件和要求都有哪些？PhD Position in Leveraging AI and Machine Learning to Investigate Shy Shark Abundance in Kelp Forests项目是不是全奖？有没有奖学金？下面我们一起看一下雷丁大学申请Phd直招需要具备哪些条件和要求，以及托福、雅思语言成绩要到多少才能申请。
申请要求
This project would be suitable for students with a degree in computer science / AI Technology or a closely related field in environmental / physical science, or with expertise in shark biology or marine science/biology.
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