Sort by
Refine Your Search
-
Listed
-
Employer
- ;
- UNIVERSITY OF SURREY
- QUEENS UNIVERSITY BELFAST
- University of Nottingham
- University of Surrey
- Cranfield University
- Nottingham Trent University
- UNIVERSITY OF SOUTHAMPTON
- Aberystwyth University
- CRANFIELD UNIVERSITY
- KINGS COLLEGE LONDON
- King's College London
- Oxford Brookes University
- The University of Southampton
- University of Brighton
- University of Bristol
- University of Stirling
- University of the West of England
- 8 more »
- « less
-
Field
-
expected to apply for funding to build and supervise a research group. Three main research areas will be explored: Multimodal data integration – the postholder will adapt and apply modern AI architectures
-
, Radio Resource Management (RRM) for 5G-NR systems. The post holders will be expected to design, develop and validate efficient processing architectures in the context Open RAN based 5G-NR systems
-
, Radio Resource Management (RRM) for 5G-NR systems. The post holders will be expected to design, develop and validate efficient processing architectures in the context Open RAN based 5G-NR systems
-
, simulation-based testing, including test generation, formal specification and verification of complex designs, and computer architecture. You have excellent research skills and experience of independent
-
. Proficiency with handling image file formats deriving from commercial scanners for pathology slide image generation. Comprehensive experience in handling segmentation-based architectures, both convolutional
-
Resource Management (RRM) for 5G-NR systems. The post holders will be expected to design, develop and validate efficient processing architectures in the context Open RAN based 5G-NR systems focusing on PHY
-
Resource Management (RRM) for 5G-NR systems. The post holders will be expected to design, develop and validate efficient processing architectures in the context Open RAN based 5G-NR systems focusing on PHY
-
will design and implement an edge-based architecture for applications using Federated Learning (FL) that will be accessible to resource-constrained end nodes, e.g., IoT devices. Additionally, REMINDER