Postdoctoral Positions in Environmental and Ecological Modelling, Canada

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    cbauch
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    Postdoctoral Positions in Environmental and Ecological Modelling at the University of Guelph, Canada

    We are seeking 1-2 postdocs for research in climate modelling and/or ecological modelling, under the co-supervision of Prof. Madhur Anand (Global Ecological Change & Sustainability Laboratory, School of Environmental Sciences, University of Guelph: https://www.uoguelph.ca/~manand/Madhur_Anand/Welcome.html) and Prof. Chris Bauch (Department of Applied Mathematics, University of Waterloo: https://www.math.uwaterloo.ca/~cbauch/index.php).

    Applicants will be working on one or both of the following topics:

    1.  Modelling interactions between climate dynamics and social processes, with application to climate policy (e.g. https://doi.org/10.1371/journal.pcbi.1007000, )

    2.  Modeling interactions between terrestrial processes and climate dynamics, for use in machine learning approaches to predict climate change tipping points (e.g. https://doi.org/10.1073/pnas.2106140118).

    Possible modelling approaches include differential equation models and agent-based models.

    Applicants should have an outstanding publication record, and strong skills in oral and written communication, literature review and synthesis, team collaboration, and initiative.  Applicants must have background training or research experience that is directly relevant to these topics.

    Successful applicants will be appointed in the School of Environmental Sciences, University of Guelph, with the possibility of a visiting researcher appointment at the Department of Applied Mathematics, University of Waterloo. Many collaborative opportunities are available. Please send enquiries with CV to manand@uoguelph.ca and cbauch@uwaterloo.ca to initiate discussions. Enquiries will be reviewed beginning immediately until a suitable candidate is found.  Salary is commensurate with experience.  Positions are for one year, with the possibility of a second year subject to satisfactory performance.

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