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Field
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working with NLP in general and LLMs in particular. They will also help to further develop machine learning models to predict clinical outcomes. Familiarity with current methods in this area is essential
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cerebral organoid models to study the impact of environmental exposures on metabolostasis and proteostasis Build and validate machine learning models (in collaboration) for molecular data analysis and
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and refine algorithms and models for large-scale language processing tasks, with a focus on healthcare data Contribute to developing new models, techniques and methods for clinical machine learning
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and the analysis of large-scale assessment data: Methods and approaches to synthesize large data sets via meta-analyses (e.g., meta-analyses of large-scale assessment data, meta-analyses of meta
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the University's research culture and collaborative profile. Qualifications: PhD in Computer Science/AI or a closely related field. Extensive research experience in machine learning, deep learning, and self
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personal qualifications A PhD in data science, statistical genetics, quantitative genetics, bioinformatics, statistics, computer science, or closely related fields (required). Experience with large-scale
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personal qualifications A PhD in data science, statistical genetics, quantitative genetics, bioinformatics, statistics, computer science, or closely related fields (required). Experience with large-scale
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-scale assessment data, meta-analyses of meta-analyses) Methods and approaches to cumulative, living, and community-augmented meta-analyses Methods and approaches to include machine learning and artificial
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may include but are not limited to: algorithm and software development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning
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transportation operations and network modelling, accessibility analysis, data analysis (statistics and/or machine learning methods), and spatial mapping. Because the work will involve multiple years of daily