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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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minimum qualifications at the time of hire. PhD in computer science, data science, or related discipline Track record of publications in Artificial Intelligence and Deep Learning in peer-reviewed
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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research infrastructure. Apply advanced statistical, machine learning and data engineering methodologies to large-scale, longitudinal datasets, contributing to innovative melanoma and skin cancer research
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analysis Machine learning and retrieval-augmented AI models for biomarker prioritization and decision support ·Work closely with cross-functional team members to develop hypotheses, interpret data, and
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biomarkers associated with adverse pregnancy and neonatal outcomes. Apply statistical modelling, machine learning, network analysis, and systems biology approaches to large-scale datasets. Perform longitudinal
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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: Guide junior researchers and graduate students. Required Knowledge, Skills and Abilities AI/ML Expertise: Strong knowledge of advanced machine learning, deep learning, and AI techniques. Programming
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quantitative genetics, Bayesian methods, machine learning, large-scale genomic datasets, single-cell omics or integrative omics analyses would be highly regarded if the candidate was not initially trained in
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areas of statistics, data science, and artificial intelligence (AI). The position will focus on developing and applying novel statistical, machine-learning, and AI methods to advance biomedical and