Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- Cranfield University
- Loughborough University
- University of Nottingham
- University of Birmingham
- University of Birmingham;
- University of Sheffield
- KINGS COLLEGE LONDON
- University of East Anglia
- ;
- ; Swansea University
- King's College London
- Kingston University
- Oxford Brookes University
- UCL
- University of Cambridge
- University of Cambridge;
- University of Essex
- University of Greenwich
- University of Newcastle
- University of Reading
- University of Warwick
- 11 more »
- « less
-
Field
-
marine sciences, biological oceanography, ecology, or computer sciences. Strong analytical, numerical and practical skills are essential. Experience in coding or applying quantitative methods in a
-
(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary
-
quality, diversity, and biological relevance using standard metrics and expert review. Anonymised digital images from tissues in biobanks will be used to train generative models on university computing
-
expertise in AI and biomedical computing at the School of Biomedical Engineering & Imaging Sciences. The work will be done in close collaboration with a multidisciplinary team at KCL, UCL and with clinicians
-
: environmental monitoring, AI, computer vision or multispectral imaging. Entry Requirements At least UK equivalence Bachelors (Honours) 2:1. English Language requirement (Faculty of Science equivalent: IELTS 6.5
-
scanning confocal microscopy and calcium imaging in time-lapse, computational imaging approaches for analysis of images and movie recordings, analysis of the connectome to identify neural circuits
-
develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
-
This PhD project focuses on advancing computer vision and edge-AI technology for real-time marine monitoring. In collaboration with CEFAS (the Centre for Environment, Fisheries, and Aquaculture
-
in AI. Previous publication record in relevant fields: AI, machine learning, computer vision, etc. Previous successful project on a relevant topic. Good knowledge of statistics, probability
-
This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at