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–functional modeling of root system architecture. Phenomics data integration and high-dimensional trait analysis. Predictive breeding and quantitative genetic modeling. Machine learning approaches to genotype
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Computer Engineering or a closely related field, completed by the start date of the appointment. U.S. citizenship is required. Research experience in at least one of the following areas: ● Chip design
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, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing multiscale simulation methods for quantum mechanical
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, training, learning technologies, digital learning, workforce development, or human-computer technology design. Experience with translation, applied, or design-based research. Experience with data analysis
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spearhead technical development, data analysis, and publication efforts for a student-led initiative using OpenBCI hardware, EEG recordings, eye-tracking integration, and machine learning to enhance neural
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expertise in integrating advances in biomedical engineering, technology, and Artificial Intelligence (AI) and Machine Learning (ML) methods to tackle complex biomedical challenges in nutrition and health
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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work is studying how undergraduate STEM courses can be tailored to meet the needs of all learners. Current projects involve studying STEM instructor perceptions of student-centered learning as
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multi-omic sequencing, network biology, and machine learning to identify actionable biomarkers and therapeutic vulnerabilities. The successful candidate will work at the interface of computational
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/Licenses Required Knowledge, Skills, and Abilities Experience in metabolomics and flow cytometry (FACS) analyses, or the ability to rapidly acquire these skills, is expected. Effective oral and written