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We have 55 Chemical Engineering (learning) PhD Projects, Programmes & Scholarships

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Chemical Engineering (learning) PhD Projects, Programmes & Scholarships

We have 55 Chemical Engineering (learning) PhD Projects, Programmes & Scholarships

Predicting drug solubility in different solvents using molecular simulation and machine learning

Predicting the solubility of complex drug-like molecules is crucial at several stages of the drug discovery and manufacture. In particular, solvent selection has been highlighted as a crucial step in process design and optimisation [1]. Read more

Accelerated Discovery of Next Generation Polymers Using Artificially Intelligent Reactor Platforms

  Research Group: School of Chemical and Process Engineering
The application of digital technologies is transforming the discovery and manufacturing process within materials science. This therefore represents perhaps the only opportunity to address the urgent need for new sustainable, high performing materials to facilitate more efficient processes (e.g. Read more

Particle properties by design

  Research Group: School of Chemical and Process Engineering
This interdisciplinary project presents an exciting opportunity for an ambitious scientist or engineer to work across the boundaries of chemistry, physics and engineering, with opportunities to develop a broad portfolio of skills. Read more

Molecular Simulations and Data-driven Modelling for Polymer Nanocomposite Membranes

The urgent need to mitigate climate change has led to increased focus on sustainable energy solutions. This includes the development of mixed matrix membranes (MMMs) for gas separation in net-zero applications. Read more

PhD Research Scholarship Opportunity in NMR analysis of illicit drugs

We’re looking for a highly-motivated PhD applicant to join our multi-disciplinary team, to work on developing a field-invariant NMR spectroscopy database for the analysis of illicit drugs. Read more

Metamaterials for catalysis

A meta-atom is an engineered sub-wavelength structure that, when periodically arranged like an atomic lattice, enable optical, electronic, thermal, and chemical functionalities not seen in conventional designs. Read more

Microbubble Coalescence in Turbulence

Supervisory Team.   Dr John Lawson. Project description. Applications are invited for a fully funded PhD position on applying computer vision (CV) and machine learning (ML) approaches to experimentally measure and model coalescence in turbulent dispersed multiphase flow. Read more

Data centric modelling of adhesive wear

Supervisory Team:  Robert Wood, Jo Grundy and Dave Stewart (RRSL).  Project description. The occurrence of adhesive wear between sliding surfaces can cause high friction, vibration, material transfer between surfaces and even seizure of components. Read more

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