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Field
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PhD studentship in Computational Chemistry – Training force fields for computer-aided drug design with machine learning. Award Summary 100% fees covered, and a minimum tax-free annual living
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wall metabolism and membrane integrity Design and synthesise small libraries of peptide analogues Screen compounds against drug-susceptible and drug-resistant clinical isolates Develop combination
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mechanisms to guide the design and formulation of nanoparticles with superior drug delivery properties. Investigate the interaction between ultrasound, nanoparticles, and biofilms to assess antimicrobial
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annually, with life-threatening consequences for immunocompromised individuals. With few antifungal drugs available and resistance on the rise, this project explores a novel strategy: engineering the fungal
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Europe. Through an extensive training program, 10 PhDs will be delivered to the European R&D ecosystem, with expertise in low data ML with domain knowledge in synthetic organic chemistry and drug discovery
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for developing new treatments against drug-resistant infections. Their rapid action and ability to target bacteria in several ways make it difficult for antimicrobial resistance to emerge. Despite this promise
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the side effects that are often seen with classical cancer drugs? Over the years there have been a number of approaches to developing targeting methods. We have previously used peptides to generate molecules
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Date 1 Feb 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description We
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organoids. The insights generated will speed up drug design and development and push forward our understanding of human health and disease. As a Franklin PhD student you will: Work on highly interdisciplinary
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that block infection at its source, with broad applicability to respiratory pathogens, including drug-resistant bacteria. Approach and Methods: Use automated synthesis platforms to generate a high-throughput