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Field
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evaluate large language models (LLMs) assisted methods for extracting, organising and assessing complex scientific information related to materials. The initial application will be phase diagrams and related
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of novel methods to decompose the heterogeneity of psychiatric disorders, particularly ADHD, using large-scale genetic, imaging, and epidemiological data from Denmark and other countries. You will apply
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11.11.2024, Academic staff In the project “BIG-ROHU” (BIG Data - Rotor Health and Usage Monitoring), a system is being developed which provides information on both the health and the actual stress
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healthy despite being at high genetic risk of dementia? This project will use cutting-edge genetic and molecular data from large human biobanks to identify molecular factors that help protect against
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/Qualifications Bachelor's or Master's degree in a field that enables computational analysis of large datasets (e.g., bioinformatics, computational biology, data science, computer science, computer engineering
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from animal hosts (are ‘zoonotic’), yet it is hard to predict which pathogen species are likely to cross into human populations and why. This project will combine AI Large Language Models, extracting
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management High-performance computing and large-scale data analysis Innovation, entrepreneurship, and technology transfer Scientific communication and transferable skills Training activities will be organised
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heterogeneous sources of information—including remote sensing, forest inventory data, existing forest maps, environmental and climatic data, and emerging large-scale AI representations—can be jointly exploited
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for place with the projects across the MRC GW4 BioMed3 Doctoral Landscape Award studentships. There are a total of 18 studentships available across the partnership. This exciting PhD combines big data and
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knowledge graph from scientific papers and cognitive test questionnaire data, and second, to integrate the graph with transformer-based large language models and causal learning. This offers an explainable