49 algorithm-development-"Prof"-"Prof"-"Washington-University-in-St" PhD positions at Cranfield University
Sort by
Refine Your Search
-
) of high-value critical assets. Through this PhD research, algorithms and tools will be further improved and developed, validated and tested. It is expected that combining the domain knowledge and the
-
and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining
-
vulnerabilities like side-channel attacks and unauthorized access, which can compromise system integrity. Developing robust security measures within AI-enabled electronics is essential for applications in defence
-
sources compared with gas turbines, etc. The aim of this PhD research is to develop novel performance simulation capabilities to support the analysis and optimization for sCO2 power generation systems
-
project will develop novel methods for modelling and controlling large space structures (LSSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. Working with leading
-
expertise. We link fundamental materials research with manufacturing to develop novel technologies and improve the science base of manufacturing research. The Through-life Engineering Services (TES) Centre
-
to develop cutting-edge quantitative and applied environmental skills with real-world impact. It is a fully funded NERC CENTA PhD Studentship for 3.5 years with CASE support from BASF. Successful home-fees
-
this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
-
to slower, longer-term, evolution (e.g. vegetation and morphological change). For some, blue spaces may be perceived as ‘wasted space’, whilst highly engineered systems may be perceived as natural
-
be developing advanced spatial models such as graph-based approaches and network analytics to predict how blue network dynamics, fragmentation and surrounding land use interact to shape ecosystem