Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
techniques (e.g., ultrasonic C-scan, X-ray CT, thermography) rely heavily on expert interpretation, are time-consuming, and often fail to detect subsurface or latent damage accurately. Advances in artificial
-
University and around the globe. Find out more about the specialisms within the Centre (Centre for Computational Engineering Sciences) and please contact Professor Karl Jenkins to discuss undertaking an MSc by
-
, or problem. Clearly state the research aim and three or four supporting objectives. Establish the relevance and value of the proposed research question, highlighting its originality and significance. Outline
-
trust in digital communications and readily bypass conventional security controls. This PhD research proposes to design, develop, and validate a novel, explainable, multi-modal detection framework. By
-
induction and embedding into Cranfield University and Water Resources West. •Develop research aim and objectives and outline methodological approach •Conduct evidence synthesis of relevant literature
-
. Our Values and Commitments Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. Find out more here . We aim to create and maintain
-
. The project delves into areas such as hardware-based security measures, tamper detection, and the integration of explainable AI models within embedded platforms. Situated within the esteemed IVHM Centre and
-
control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
-
on food safety, climate-resilient agriculture, and regulatory controls, accurate detection and risk assessment of such mycotoxins have become critical components of modern food science, toxicology, and
-
This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project