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-driven model selection, and deep learning for data analysis and feature extraction from characterisation data. Surrogate modelling will be employed to reduce computational costs, and AI-based uncertainty
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recommend that you apply early as the advert may be removed before the deadline. This fully funded PhD position, offers an exciting opportunity to develop and optimize multiscale models for surfactant
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an emphasis on reaction development and optimization. Training BioProcess aims to train the next generation of bio-innovators. Our interdisciplinary programmes prepare PhD students and researchers with the real
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Pharmaceutical Roundtable. In this project we will employ deep learning-based protein sequence design tools to deliver biocatalysts for peptide synthesis. These designed enzymes will be further optimized using
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particular, we will use topology and shape optimisation methods to compute the optimal domain shapes that can stabilise solutions with desired/prescribed properties. We will use methods from inverse problems
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19 Jan 2026 Job Information Organisation/Company The University of Manchester Department Computer Science Research Field Computer science Researcher Profile First Stage Researcher (R1) Positions PhD