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work on the INSTINCT-MB programme, which brings together teams based at Newcastle University, The Institute of Cancer Research and University College London. The programme will generate a wide range of
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the structure and function of chromatin-associated complexes involved in gene repression. About Us: We are a dynamic and international team working within the South Australian immunoGENomics Cancer Institute
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Engineering, Operations Research, Civil Engineering, Computer Science, Data Science or a related field, from a university/department with a strong international research reputation Strong mathematical and
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Project details: Surface features are important in additively manufactured parts. While additive manufacturing technology has made great strides in the realisation of complex shapes, topologies and
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candidate with a strong background in computational fluid dynamics (CFD) and specialized expertise in hemodynamics associated with coronary artery disease (CAD). The ideal candidate will hold a PhD in
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the ranking. However, STV method becomes considerably more complex with encrypted ballots. Our goal is to develop an algorithm/protocol to count encrypted ballot using the STV method. Our first point of
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start dates: 1 October 2025 (Enrolment open from mid-September) Supervisors: Hari Arora (Biomedical Engineering), Richard Johnston (Materials) and Iain Whitaker (Medicine) Aligned programme of study: PhD
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PhD Position - Organic Electrosynthesis: monitoring of reaction transients with real-time techniques
between electrolyte-electrode interface structure and selectivity or efficiency of chemical outcome during the preparation of complex and value-added compounds directly from the readily available feedstock
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
Your Job: The field of structural DNA nanotechnology holds a great promise for the realization of all-DNA building blocks with arbitrary complexity and shape at the sub-nanometer scale
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design are often too slow, costly, and inefficient to cope with the increasing complexity of performance and resource-efficiency requirements. This collaborative doctoral project brings together