Sort by
Refine Your Search
-
, reliability, and environmental resilience. The proliferation of intelligent systems has led to increased energy consumption, raising concerns about sustainability and operational costs. Energy-efficient
-
strengths and interests (e.g. geospatial data science or socio-environmental modelling). Funding Sponsored by the Leverhulme Trust and Cranfield University, this Connected Waters Leverhulme Doctoral programme
-
, finance, and healthcare, where data integrity and system reliability are non-negotiable. This PhD project addresses the integration of robust security measures within AI-enabled electronic systems
-
support from leading experts in Advanced Sensor Technology Research Group and Environmental Assessment Facilities at Cranfield University. This fully funded studentship is part of the Connected Waters
-
mitigating jamming and spoofing threats in real-time. Integration of Trusted Execution Environments (TEEs): Investigate the use of TEEs to create secure zones within embedded systems, facilitating secure data
-
This project aims to bridge the gap between technological advancements and their integration into societal and environmental systems by shifting from a product-centric to a service-oriented approach
-
, environmental engineering, environmental science or other relevant engineering/science degree. The ideal candidate should have some understanding of water science. The candidate should be self-motivated, driven
-
designing research approach and drawing on a wide range of social science methods. Key commercial sectors include (but are not limited to) data centres and high-tech industries, as well as food and beverage
-
degree or equivalent in a related discipline. This project would suit individuals with academic or industrial experience in electronics, electrical engineering, systems engineering, or AI/data analytics
-
, environmental science, urban sustainability, geospatial analysis, or quantitative modelling. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods