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We have 24 Physical Chemistry (machine learning) PhD Projects, Programmes & Scholarships

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Physical Chemistry (machine learning) PhD Projects, Programmes & Scholarships

We have 24 Physical Chemistry (machine learning) PhD Projects, Programmes & Scholarships

Machine Learning and Molecular Modelling in Mass Spectrometry

This PhD project will harness the power of computational modelling and machine learning (A.I.) to analyse data obtained by mass spectrometry experiments and predict structural characteristics of biomolecules and their interactions. Read more

Machine learning analysis of large-scale crystal structure prediction

Supervisory Team:  Prof. Graeme Day. Project description. A fully funded 3.5-year PhD studentship is available in the area of computational materials discovery, as part of a prestigious international Synergy grant funded by the European Research Council. Read more

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

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

PhD Studentships - Process Industries: Net Zero Data Driven Optimisation for Process Scale-up

Award summary. These studentships provide 100% fees (Home & international), a minimum tax-free annual living allowance of £18,622 (2023/24 UKRI rate), and a research training support grant of £20,000. Read more

Experimental Discovery of New Ionic Conducting Materials Towards Net-Zero Technologies

Materials that allow the rapid motion of ions are essential for the new energy technologies needed to meet the challenge of net zero, such as batteries, fuel cells and electrolysers for green hydrogen. Read more

Discovery of Functional Inorganic Materials for Net Zero Applications using High-Throughput Synthesis

This project will use high-throughput solid state synthesis methods developed in the group (Hampson 2023) to accelerate the discovery of new functional inorganic (oxide) materials for applications towards net zero technologies e.g. Read more

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