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AgriLife and make a difference in the world! Position Information: The successful candidate will contribute to developing and evaluating terrestrial and aquatic nitrogen dynamics and emission modeling, with
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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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consequences of DNA methylation on risk for depression in adolescence and young adulthood. Projects aim to integrate longitudinal data with insights from genetics, epigenetics, and human development to identify
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microscopy data analysis, chemometrics, and machine learning. This position is ideal for a researcher who enjoys working at the interface of imaging, data science, and environmental monitoring. The project
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learning methods and omics data to nominate candidate drivers of islet responses, identify potential conditional dependencies between them, and validate them using wet-lab experiments in mice and human
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machine learning. We focus on inductive logic programming (ILP), which learns logical rules from data. We primarily use automated reasoning techniques, such as SAT/ASP/SMT/MaxSAT solvers, to learn rules
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and machine learning. We focus on inductive logic programming (ILP), a form of inductive program synthesis which learns logical rules from data. The focus of this position is to develop ILP/program
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develop digital twin algorithms to assist clinicians in developing treatment plans for patients with chronic diseases. Analyzes complex sensor data, works with a multidisciplinary team to develop health
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PIs, publish manuscripts, and apply for career development grants. Develops statistical and generative AI models for single-cell and spatial data from the Martin Lab and multimodal (genetic, imaging