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to translate ground-breaking technologies into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too
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that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word “challenging,” and we love
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be a large amount of qualitative data analysis required, potentially using framework analytic approaches. In addition, evidence synthesis skills such as systematic reviews and meta-analysis, and
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analysis of large, diverse datasets including field experimental data, geospatial data, and time series data. Experience with machine learning and statistical learning. Familiarity with various management
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performance. The research will involve working with large-scale datasets and may include the use of advanced data analysis or AI-based methods to support modelling, uncertainty analysis, and systemic risk
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environments. Demonstrated experience in data engineering and ETL workflows required to prepare large, real-world dataset for machine-learning development is also essential. Please note experience in healthcare
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benchmarking of large language models (LLMs). The research profile of the group is heavily machine-learning oriented and the group has access to excellent HPC infrastructure. For more information about LTG
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to translate ground-breaking technologies into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too
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institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences, and
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the application of analytic techniques to longitudinal or large mental health data sets or biobanks a strong applied research orientation with a proven ability to build successful working relationships with a