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experience in designing and implementing interactive data visualizations using common libraries like D3.js, ggplot, or vega, as well as expertise in languages and libraries for data analysis such as R
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combining artificial intelligence with human expertise. The lab develops knowledge-grounded artificial intelligence, text mining and knowledge assembly, mathematical and systems modeling, and causal analysis
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and data science. This cycle's focus is on AI-powered data analysis to advance hypothesis-driven research related to addiction. Computational analysis of previously collected data can increase the speed
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projects that may involve concepts and priorities such as knowledge-graph-driven data integration, AI-powered literature review, agentic database search assistants, multi-omics analysis tools, life sciences
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multimodal interaction. Our research aims to bridge the gap between visual perception and actionable assistance, with applications including: · Video-based skill coaching and instruction
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dynamometry. Develop and maintain analysis pipelines and ensure data quality across sessions. Prepare manuscripts, conference abstracts, and grant applications as first author and contributing author. Develop
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in cortical circuits mediating defensive responding. Responsibilities will include rodent colony management, stereotaxic surgery, behavior testing, fiber photometry protocols and data analysis, brain
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and computation, involving the analysis of feedback controllers in systems and synthetic biology, the analysis of algorithms, and merging of ML and mechanistic approaches to deal with context effects
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conditions. Bioinformatics skills relevant to genomics-driven natural products discovery, including genome mining, biosynthetic gene cluster analysis, sequence alignment, and annotation. Strong quantitative
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. Work will emphasize the development and analysis of advanced methods in areas such as sparse signal recovery, compressed sensing, and statistical estimation, with a particular focus on their role in