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Materials Science, Characterisation Materials Science, Defence Composite Materials, Functional Composite Materials, Energy, Nanomaterials, Low Dimensional Materials, Biomaterials Materials, Biological
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an experienced, motivated quantitative Research Training Fellow to lead data integrative analyses in large-scale epidemiological studies of cancer and apply innovative methods in epidemiology, statistics and data
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Materials Science, Characterisation Materials Science, Defence Composite Materials, Functional Composite Materials, Energy, Nanomaterials, Low Dimensional Materials, Biomaterials Materials, Biological
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manufacturing of wood-polymer composites (WPCs). The successful candidate will explore and optimise biodegradable WPCs and wood waste powder size and aspect ratio for use in extrusion and powder-bed fusion
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To design and fabricate a novel biomimetic honeycomb composite material that synergistically integrates efficient electromagnetic wave absorption, superior thermal conductivity, and hierarchical
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petrological, whole-rock- and mineral-geochemical investigations of cores and cuttings to determine the composition of the sedimentary rocks, their provenance, and their diagenetic evolution construction
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single-cell genomics, multi-omics integration, or statistical genetics Familiarity with large-scale human datasets (e.g., All of Us, UK Biobank, dbGaP) Experience in autoimmune, immunometabolic
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multi-omic or spatial profiling). Experience with high-throughput screening approaches or analysis of large-scale genomic or clinical datasets. Proficiency in R and/or Python for bioinformatic analysis
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of the criteria and the final classification will be based on a scale from 0 to 100, with scores rounded to the nearest hundredth, applying the following formulas: - AR: criterion 1 * (60%) + criterion 2 * (40
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) will also be valued. VII.III- The evaluation of the criteria and the final classification will be based on a scale from 0 to 100, with scores rounded to the nearest hundredth, applying the following