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, a strong degree in computer science, cybersecurity, mathematics, or a related subject. Experience with cryptography, machine learning, or systems implementation is valuable, as are solid programming
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Campylobacter disease burden is assessed, identify drivers of change and possible interventions. You will explore how genomic diversity relates to clinical outcomes, whether machine‑learning approaches can
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join the internationally recognised EnDROIDS programme (https://endroids.ico2s.org ) and become part of a highly interdisciplinary team of computer scientists, molecular biologists, DNA nanotechnologists
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studies have highlighted vulnerabilities in learning‑enabled CPS, demonstrating that small, carefully crafted perturbations can cause unsafe or malicious behaviours. However, much of the literature focuses
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-earth elements (REEs) are essential to key sectors in the modern economy including the electronic, energy, pharmaceutical and automobile industries. Despite their importance, these critical elements
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Eligibility Criteria You must have, or expect to achieve, at least a 2:1 Honours degree in biology, genetics, biomedicine, or a related area. The candidate must be willing to learn bioinformatics. A further
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Honours degree in biology, genetics, biomedicine, or a related area. The candidate must be willing to learn bioinformatics. A further qualification such as an MRes is advantageous. The studentship covers
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models and observations yet still rely heavily on precomputed risk maps. Deep learning has improved flood mapping, but many models remain event-specific and transfer poorly between regions. Geospatial
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, their empirical security assessment remains underdeveloped, representing a growing and critical gap. Recent studies have highlighted vulnerabilities in learning‑enabled CPS, demonstrating that small, carefully