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We have 12 Computational Chemistry (e learning) PhD Projects, Programmes & Scholarships

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Computational Chemistry (e learning) PhD Projects, Programmes & Scholarships

We have 12 Computational Chemistry (e 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

Mathematical Machine Learning for Molecular Modeling

Project description. This PhD project aims to develop Machine Learning methods for Molecular Modeling with a particular focus on aspects relevant to dynamics preserving coarse-graining strategies. Read more

Atomistic Simulations of Surface Chemistry underpinning the Atomic-Scale Processing of Materials for AI-driven Nanoelectronics Applications

Project description. Atomic layer deposition (ALD) and atomic layer etching (ALE) are crucial technologies in semiconductor processing, especially as nanoelectronics devices become smaller and more complex. Read more

Design and Synthesis of Peptide-derived Receptors for Binding Small Molecules

A PhD studentship is available in the groups of Prof. Scott Cockroft and Dr Annamaria Lilienkampf School of Chemistry, The University of Edinburgh; https://www.cockroft.chem.ed.ac.uk in collaboration with DSTL. Read more

PhD Studentship in the atomistic dynamics of mechanochemical reactivity

Applications are sought for a fully funded 3.5-year PhD position in the group of Dr Adam Michalchuk in the School of Chemistry at the University of Birmingham to start Oct. Read more

Enabling CO2 Capture and Storage Using AI

Carbon Capture and Storage (CCS) is one of the viable solutions that can effectively reduce CO. 2.  emissions from hard to decarbonise industries (e.g., steel, cement, power generation). Read more

Computation-driven Design of Functional Materials

The urgent demand for new technologies has greatly exceeded the capabilities of current materials research. In the past decades, computational modelling has been playing an increasingly important role in accelerating materials discovery and innovation. Read more
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