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teams. Unit URL https://imci.uidaho.edu/ Position Qualifications Required Experience Experience with statistical or predictive modeling as demonstrated by publications in the field Evidence of strong
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
The successful candidate is expected to: Build and evaluate chemical databases based on experimental and computational data. Establish predictive models based on artificial intelligence methods. Utilize
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of experimental and/or simulation data, the development of predictive models linking cellular responses to the properties of the surrounding environment, and the implementation of explainable AI approaches
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, engineering, physical or life sciences. ● Demonstrated experience with laboratory experiments (design, conduct, analysis). ● Demonstrated experience in modeling (formulating equations, and computing predictions
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, or digital health. Responsibilities The Postdoctoral Scholar will: Analyze large-scale datasets from cohorts and clinical trials, including longitudinal data. Develop and implement predictive models
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multiple Jurassic cold snaps using stable carbon isotope fractionation measured in molecular fossils e.g. phytane. Integrated with Earth System models, this approach allows to quantify carbon–climate
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learning, understand their mathematical foundations, and connect them to space-related technologies and missions. The focus is on building rigorous models that explain and predict the behaviour of modern
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understand why species and populations differ in their vulnerability, so that research can inform conservation, management, and policy. However, we still cannot reliably predict how climate change will affect
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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | about 2 months ago
and modeling of RESOLUTE data (https://re-solute.eu/ ) together with metabolic, structural, genetic and clinical datasets to generate new insights into transporter biology: how cells regulate access
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analysis, while other competence areas include e.g. causal inference, latent variable modelling and prediction models. The section aims to maintain a strong position in these areas but also wants to expand