55 web-developer-"https:" "https:" "https:" PhD positions at The University of Manchester
Sort by
Refine Your Search
-
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
-
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
-
this, empirical rules have been developed. However, a fundamental understanding of the process is still lacking. Furthermore, current standard tests do not adequately capture the phenomena, and thus industry is
-
. Chem. Soc. 2021, 143, 9813), and developing their reactivity (e.g. Nat. Commun. 2020, 11, 337). Nevertheless, there are still many elusive actinide-ligand multiple bonds that would be ground-breaking
-
, stiffness loss, damage evolution, and transient creep interact under coupled loading. The project will develop temperature-dependent constitutive models informed by numerical simulation. Machine learning
-
digital twins with quantified uncertainty. This project will develop a measurement-science-driven digital twin framework for energy assets, initially demonstrated on PV modules/fields and battery systems
-
the most efficient perovskite PVs rely on environmentally harmful compounds. Developing stable and eco-friendly materials and devices could lead to significant breakthroughs for the future photovoltaics
-
clinical applications. This PhD studentship will develop next-generation polymer drug delivery implants designed to form in situ and enable tuneable release pathways. By controlling polymer architecture
-
they operate their aircraft. This project aims to develop metallic coatings for corrosion protection of components used at high temperature; similar environment as in aero-engine. The project will involves close
-
, heat removal, and temperature feedback. This PhD will develop and validate an integrated, computationally efficient modelling workflow for monolithic HPCR systems, coupling deterministic reactor physics