Background: In our daily life such as in a crowded city, we may never notice minor changes every day which are possibly beyond our perceptible ranges. In contrast, when we look down from the sky (probably like a God?), it is much easier to discover and understand the changes deeply hidden in the soils of cities, countryside fields, rivers, lakes, and forests, mostly thanks to aerial and satellite imaging. Particularly, we can collect these imagery records over years and compare them with each other to reveal hidden variations and alarming environmental impact.
Research focus: In this project, we will leverage the newly developed AI methods such as deep neural networks (DNNs) to unlock the subtle variations from soil signals, e.g. using wide-spectrum high-resolution images, and identify fundamental and long-term impact of our social activities on the nature, for example, global warming and micro-plastic pollution at large scales such as cities or beyond.
Specifically, we aim to propose novel evolving DNN models incorporated with evolutionary computation techniques to capture subtle signal variations that can potentially reveal the long-term impact of global warming, flooding, micro-plastic pollution, and so on. Based on our finding, we will develop an AI-based model for environmental sensing, monitoring and prediction from soil signals.
Training and Skills: The PhD candidate will work with a multidisciplinary supervision
team with world-class researchers in the field and receive specific research training on soil ecology, bioinformatics, biogeochemistry, deep learning, machine learning, evolutionary computation, computer vision and image processing techniques, as well as multidisciplinary training in SIGs, LiDAR and high-throughput sequencing technologies
The candidate is expected to have a Computer Science or Engineering degree with some basic knowledge on artificial intelligence, machine learning, and data mining, as well as Python, Matlab, and Java progamming skills. It is desirable that the candidiate has research expertise in soil ecology, deep learning, computer vision and image processing.
For more information, please contact Dr Li Zhang ([email protected]
Eligibility and How to Apply:
Please note eligibility requirement:
• Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. OP.....) will not be considered.
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.