Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
optimising performance across multiple timescales and spatial domains. Systematically resolving these challenges in renewable-dominated power networks stands as a critical cornerstone for enabling the roadmap
-
—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
-
PhD Studentship: Multi-robot Cyber-physical Systems for Solar Farm Health Monitoring and Maintenance
This studentship is offered by the EPSRC Centre for Doctoral Training in Robotics and Artificial Intelligence for Net Zero Centre (RAINZ) which is a partnership between three of the UKs leading
-
complex, involving multiple senders and receivers interacting simultaneously within a dynamic network. Social groups also exhibit preferred and avoided associations, creating heterogeneous social structures
-
and prosthetic devices in the real-world. This PhD project offers the opportunity to work on pioneering research that combines state of the art computational modelling (deep neural networks) and
-
that are potentially present in the EoL scrap stream through single and multiple deliberate additions. To use advanced characterisation techniques to investigate features in the microstructure that are introduced by
-
” highly flexible components and ensure their energy harvesting capabilities. This project will contribute to ambitious plans for SBSP as a vital part of the future Net Zero landscape. Recent advances in
-
optimising performance across multiple timescales and spatial domains. Systematically resolving these challenges in renewable-dominated power networks stands as a critical cornerstone for enabling the roadmap
-
3 Mar 2025 Job Information Organisation/Company IMDEA Networks Institute Department Distributed Systems and Networks Group Research Field Computer science » Other Researcher Profile First Stage
-
Immunology and Computational Biology Reference number: 2025-0104 We are deploying advanced in vitro and in vivo model systems, genetic perturbations and single cell technologies with spatial readouts to study