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part of the international Refuge-Arctic project (https://www.refuge-arctic.ulaval.ca ) with links to the NASA FORTE project, whose overall objective is to better understand and predict the role played by
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develops predictive, multi-scale computational frameworks to guide sustainable microbial food production. By coupling data science with mechanistic models, this collaboration between universities, research
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transitions in and out of campus housing, accurate data reporting, and collaborative partnerships across departments. As part of our integrated residential education model, you’ll work closely with professional
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workflows that integrate modern AI and machine learning concepts (e.g., surrogate models, adaptive sampling strategies) into the drug discovery pipeline to increase throughput and predictive accuracy
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clinical study to develop prediction models for periodontal disease progression (funded by the National Institute of Dental and Craniofacial Research). Primary responsibilities of the Study Coordinator will
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research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT. The Research Engineer (Modelling and
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developing models that predict the effects of variants. We tackle this challenge via two main directions: (1) developing efficient pangenomic data structures and evolutionary models, and (2) designing deep
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analytical approaches and technological tools (e.g., artificial intelligence, remote sensing, environmental informatics, predictive modeling, and/or environmental genomics). Research should address pressing
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, and clinical data. - Apply machine learning and foundational modeling to support predictive or exploratory analyses. - Collaborate with interdisciplinary teams to refine multi-modal pipelines
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to shape disease risk. Yet most clinical risk models ignore this exposome. In BEE, we will build explainable, physics-guided, GeoAI-driven models that: Predict acute and chronic NCD risks at the population