Sort by
Refine Your Search
-
. Working at the intersection of water engineering, environmental microbiology, robotics, and lifecycle analysis, you will evaluate autonomous underwaterskimming robots that minimise energy use and chemical
-
challenge in the UK's Net Zero transition. Current satellite dependent navigation remains vulnerable to interference, jamming and signal degradation, causing serious problems for safe and efficient transport
-
-on experience with real-world SCADA data, industry collaboration with RES Group, and training in high-fidelity simulation environments (OpenFAST, Digital Twin technology). This opportunity is ideal for those
-
researcher with expertise in communication, project management, and leadership. You will build a robust national and international network and acquire advanced knowledge essential for implementing critical
-
metascintillator prototypes towards clinical application. The research will combine experimental surface engineering, advanced materials characterisation (SEM, XRD, spectroscopy), and performance testing alongside
-
, and materials science, with a strong publication record (h-index 36, i10-index 69). The second supervisor is Dr. Indrat Aria, a materials scientist with expertise in low-dimensional nanomaterials and
-
in radiation–matter interactions, computational modelling, and materials science, with a strong publication record (h-index 36, i10-index 69). Dr Francesco Fanicchia, Research Area Lead: Material
-
, including SuperMagdrive, operates using metallic propellant, offering density, integration, and safety advantages compared to conventional launcher propellants such as hydrazine. However, metal propellants
-
; gender diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working
-
-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique