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primary focus is on land use change modeling, emphasizing peatland dynamics in Canada, using geospatial data analysis and spatial econometrics. The role is crucial to the “Modelling Peatland Disturbance
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: Machine learning/deep learning model development for biomolecular data analyses and prediction Research Area: Data science and computational chemistry Required Skills: A Ph.D. in relevant field within
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cultures of First Nations, Métis, Inuit and all First Peoples of Canada, whose presence continues to enrich our vibrant community. The University of Alberta is teeming with change makers, community builders
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(Alexander Uhl) Posting End Date June 26, 2024 Note: Applications will be accepted until 11:59 PM on the Posting End Date. Job End Date Jul 31, 2025 The expected pay for this position is $4,166.67/month. The
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/ development. Expertise in analysis and manipulation of health survey and health administrative data. Experience building or working with microsimulation models will be considered a strong asset. Background in
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The Data Sciences Institute (DSI) is a central hub and incubator for data science research, training, and partnerships at the University of Toronto. Its goal is to accelerate the impact of data
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stable isotope techniques, nitrous oxide emission measurements, and modelling nitrogen flows. Diverse cropping systems will be studied, including (but not limited to) wheat, lentil, canola, mustard, pea
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contribute to the collaborative TQT research community. Principal Investigators: David G. Cory and Lea Santos Research Area: Quantum information and many-body quantum systems out of equilibrium. Required
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facilities and a regional ocean circulation model. We are seeking candidates who can combine empirical, data science and models with theoretical approaches to lead innovative, collaborative and field-advancing
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understanding of the physics behind these codes and also how the physics is coded in order to modify the code for the new model. An additional objective will be to test the model with BBN and LSS. The research