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Field
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to solve biomedical problems, or a PhD in biomedical sciences with a strong interest to apply AI and machine learning approaches. With our strong commitment to translating research findings to actionable
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. The candidate will have the opportunity to learn and lead exciting research projects that blend methods in pharmacoepidemiology with Artificial intelligence (AI)/Machine learning (ML) techniques to generate real
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and apply methods for analyzing genomic variations, e.g. SNV, SNP, SV, CNV, methylation, and gene expression Develop tools using machine learning and AI Prepare high-impact manuscripts for publication
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and experience in natural language processing, machine learning, and deep learning. • Strong written and oral communication skills in English. • Ability to work independently and as part of a team
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at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement learning, and Data-driven modeling and control. The Post
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including transient analysis, engineered system evaluation, and machine learning applications in modeling thermal fluid behavior of interest to reactor analysis. The candidate is expected to produce papers
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sciences, computer science, machine learning, and education research. Research Themes The research themes identified for the NTO postdoc include, but are not limited to, the following: Developing
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: Artificial Intelligence / Machine Learning Knowledge Representation and NLP methods Clinical Informatics Bioinformatics Biomedical Ontology Public Health Informatics Nursing Informatics Imaging Informatics
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a PhD in agricultural and resource economics, economics, or related field by the start of the position. The ideal candidate has a strong quantitative background (econometrics and causal inference
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Computer Engineering or a closely related field, completed by the start date of the appointment. Research experience in at least one of the following areas: ● Chip design, tape-out, and testing