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advanced data science methods, including statistical modeling and machine learning approaches Developing and applying novel causal inference methods, such as target trial emulation and causal AI frameworks
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computational and data-science methods, including modern ML/AI workflows for pattern recognition, clustering, anomaly detection, and inference in large and noisy datasets. Exploratory work in quantum algorithms
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applications for a non-tenure track Assistant/Associate Research Professor, Biology and Management of Urban Pests. The successful candidate will engage with the national pest management industry and conduct high
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. Track record of successful work on interdisciplinary teams. Experience with technological transitions is a plus. Skills Demonstrated mastery of written and verbal communication. Excellent team
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extramural funding. Qualifications Ph.D. in Bioengineering, Materials Science, Chemistry, Pharmaceutical Sciences, or a related discipline, with a demonstrated track record of research publications