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-informed machine learning (PIML) with domain-specific engineering knowledge. By embedding physical laws and corrosion mechanisms into data-driven models, the research will produce more accurate
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honours degree (or equivalent) in a relevant discipline such as biology, ecology, microbiology, plant sciences, bioinformatics, or data science. The project is particularly suitable for students with
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equivalent in a subject relevant to the proposed PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work independently, excellent analytical skills
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30 Oct 2025 Job Information Organisation/Company The University of Edinburgh Department School of Engineering Research Field Engineering » Civil engineering Engineering » Geological engineering
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metagenomic datasets. This project provides an exciting opportunity to discover new bioactive molecules and develop skills across informatics, biology and chemistry. Applications are welcomed from students who
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(optional). Person specification: Prior experience in computer coding (e.g., Python, SLiM), AI modelling, and understanding of evolutionary or conservation genetics / genomics is desirable. Good teamwork
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NERC. Person Specification We seek an enthusiastic individual with a degree in geoscience, physical sciences, or computer science. Numerical literacy and experience with coding tools (Matlab or Python
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research. Specific multidisciplinary skills include: synthetic chemistry, structural molecular biology and advanced imaging; it will harness cutting edge computational tools (incl. image analyses), giving
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studentship in Synthetic andPhysical Organic Chemistry available from October 2026, to work under the supervision of Prof. Paul McGonigal and Dr Alyssa-Jennifer Avestro in the Department of Chemistry at
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across the fern lifecycle (RT-PCR, qPCR). Test to see if these ancestral genes can function in male or female meiosis through genetic engineering, swapping out the Arabidopsis gene for the fern copy