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This RAP work is supported by the Flood Hazard Identification and Mapping Program (FHIMP) of Natural Resources Canada. The successful candidate will have the opportunity to conduct research in the fields of AI-powered geospatial data analytics to map post-wildfire flood risk using different data sources, e.g., burned severity map, post-wildfire debris maps, land-cover maps, high-resolution 3D land surface information, soil information, and rainfall intensity information. The focus of this RAP research work will be on the development of AI-powered predictive models to map flood risk using multi-source geospatial data, involving geospatial data preparation, data fusion, as well as spatial-temporal AI methodology in risk prediction applications.
The successful candidate is expected to have good experience in computer programming, Python and Pytorch, spatial-temporal models, geospatial data science, and remote sensing.
In order to be considered, the candidate’s application must clearly explain how she/he meets the following essential qualifications.
The analysis and research work will be conducted at the successful candidate's university.
Student will be hired through the Research Affiliate Program (RAP) with the purpose to accomplish research and complete a thesis or dissertation. The duration of the RAP will be one (1) year, with possibility of extension depending on academic level.
Positions to be filled: 1
Your résumé.
A covering letter "maximum 2000 words including proposed research concept, publications and conference attendance if any. Please include a copy of academic transcripts."
Contact information for 2 references.
A list of the courses you have taken as well as any courses that you are taking now, or that you will be taking this academic year
Education:
Currently enrolled or eligible to enroll in a MSc or Doctorate program at a Canadian University.
A minimum Bachelor of Science degree and study/fieldwork experience related to geosciences, such as Geomatics, Software Engineering, Earth Sciences.
Experience:
• Strong computer programming experience in the design of pipelines/workflows for processing geospatial data;
• Strong experience with Python and pytorch to implement different deep learning models;
• Relevant experience in spatial-temporal predication models for environmental mapping, e.g., air quality, flood risk, and wildfire risk prediction;
• Experience with 3D environmental modeling;
• Experience in geospatial data analytics;
English essential
Information on language requirements
Knowledge:
• Knowledge of computer programming
• Knowledge of deep learning and spatial-temporal prediction models
• Knowledge about how to use geospatial data for flood risk assessment
Competencies:
• Interactive communication
• Initiative
• Research report writing skills.
Selection may be limited to members of the following Employment Equity groups: Aboriginal persons, persons with disabilities, visible minorities, women
Information on employment equity
Reliability Status security clearance - Each student hired through the Research Affiliate Program (RAP) must meet the security requirements of the position as a condition of employment. Therefore, the student will be asked by the hiring organization to complete security-related documents.
The Public Service of Canada is committed to building a skilled and diverse workforce that reflects the Canadians we serve. We promote employment equity and encourage you to indicate if you belong to one of the designated groups when you apply.
Information on employment equity
We thank all those who apply. Only those selected for further consideration will be contacted.