33 computational-physics "https:" "https:" "https:" "https:" "Ulster University" PhD scholarships at Cranfield University
Sort by
Refine Your Search
-
predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
-
accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including arc energy transfer and liquid metal convection within
-
knowledge co-evolution and addressing complex challenges in a super-intelligent society. This project is situated within the rapidly evolving field of Cyber-Physical-Social Systems (CPSS), which is of
-
-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
-
models and physics-based models. More recently, hybrid prognostics approaches have been presented, attempting to leverage the advantages of combining the prognostics models in the aforementioned different
-
of first or second class UK honours degree or equivalent in a related discipline, science (chemistry/physics) or engineering. The ideal candidate should have some understanding in the area of materials
-
: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in implementation of fusion
-
elements like Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) to secure hardware components. Embedded Trust Protocols: Design protocols that establish and maintain trust within
-
AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
-
, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled