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on the adsorption mechanisms of these molecules on the metallic surfaces. In this PhD project we will use state-of-art molecular simulation methods [2,3] to clarify the adsorption and desorption mechanisms of various
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the University of Warwick. Project outline: Modelling light-driven processes and charge transfer across molecule-metal interfaces is instrumental for the development of next-generation molecular optoelectronic
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candidate will investigate adhesin-ligand interactions at molecular and cellular resolution and assess novel anti-adhesion compounds using advanced in vitro and ex vivo models of human skin
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Andres Floto and his group members. Key responsibilities include working on deep learning, deep generative modelling, and molecular design. Additional responsibilities include developing research
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molecular weight reduction, cross-linking, and morphological alterations. Additionally, degradation of the fibre–matrix interface can occur, further compromising the mechanical integrity of these composites
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mouse models of cancer. The project will use molecular biology, protein biochemistry, and enzyme assays to further characterise this agent and understand its mechanism of action. You will manage your own
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, chemists and computational experts pioneering research using an advanced, optogenetically controlled human-based nerve-muscle model. Using functional, molecular and metabolomics technologies, integrated with
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engineering, machine learning, molecular design, and sustainability, helping to create smarter ways of identifying promising sorbents for electrochemical CO2 capture. Over the course of the project
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PhD Studentship: Preclinical modelling and therapeutic targeting of glioblastoma infiltrative margin
PhD Studentship Advert Title: Preclinical modelling and therapeutic targeting of glioblastoma infiltrative margin Supervisors: Prof Ruman Rahman, Dr Stuart Smith, Dr Phoebe McCrorie Project Overview
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clinically. We will develop cancer biomarkers through statistical models, including AI driven strategies, to predict highly aggressive tissue molecular signatures for mapping future GBM relapse, linking