20 data-"https:"-"https:"-"https:"-"https:"-"Leeds-Beckett-University" PhD positions at The University of Manchester
Sort by
Refine Your Search
-
may be removed before the deadline. Modern low-carbon energy systems such as photovoltaic (PV) arrays and battery energy storage systems (BESS) generate extensive measurement data (electrical, thermal
-
on high-fidelity modelling and test data for both metals and thermo-set composite materials. To achieve this we will explore the use of advanced genetic algorithms and/or Artificial Intelligence (AI
-
overseas. Training can be provided in computational fluid dynamics, machine learning, and nonlinear dynamics. These skills are highly valued across a wide range of industries. Recent data reveals that Fluid
-
University Belfast, University of Manchester, University of Edinburgh and University of Bristol. BioAID will train the next generation of scientists in Artificial Intelligence and data-driven approaches
-
they can reliably, affordably, and fairly support a net-zero energy system. The research will focus on how data-driven and machine-learning-based control can coordinate demand, storage, and local generation
-
FFA on different type of metallic surfaces as a function of temperature and concentration. The modelling data and principal component analysis will be used to build property-structural relationships
-
performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will
-
, and data-driven analysis. The project will be supported by a strong research environment with experience in concrete behaviour at elevated temperatures, constitutive modelling, and advanced numerical
-
its frontier by integrating mechanistic artificial intelligence with robotic additive manufacturing systems to enable intelligent metal processing. The research will develop physics-informed and data
-
reusable plaque–flow atlas. Key objectives include to: Develop automated computer aided design (CAD) and meshing pipelines to generate a library of arterial geometries representing common geometric