| Position Title: Applied Statistics – Climate Change and Agriculture
Department: Mathematics and Statistics
Description of the Area/Topic of Research:
The Department of Mathematics and Statistics at the University of Guelph invites applications for a funded post-doctoral position focused on applied statistics and machine learning for assessing the impact of climate change on agricultural productivity in the Canadian and global context. The successful candidate will be involved in the analysis of a myriad streams of environmental and geospatial data recorded on continental scales. This research project is aligned with the objectives of the Food from Thought (FfT) research program at the University of Guelph. The work will be conducted under the supervision of Dr. Khurram Nadeem, who is an FfT affiliated faculty member.
General Outline of Duties: Under the supervision of Dr. Khurram Nadeem, the successful candidate will be responsible for leading the research activities of the project, including but not limited to the following:
Gathering environmental and geospatial data from various data repositories
Restructuring/cleaning large volumes of raw data and conduct exploratory analysis
Developing statistical and machine learning models
Maintaining data deposition records and documentation
Preparing manuscript(s) and presenting findings at research conference(s)
Teaching Requirement: The successful applicant is required to teach a Statistics/Data Science course in the Department of Mathematics and Statistics.
Required Qualifications
Candidates must possess a Ph.D. in statistics, mathematical ecology/biology, mathematics, or a related discipline, with a research background in modern statistical learning methods with applications to ecological and/or environmental modeling problems. Prior experience of visualising and analysing large-scale geospatial data using geographic information system software (SAGA GIS/QGIS) is an asset.
In addition to the required qualifications, preference will be given to applicants with:
• Experience in computer programming and statistical computing (C/C++, Python, R); proficiency in Linux
• Experience developing statistical and/or machine learning models for large spatial datasets
• A publication record demonstrating the ability to lead research related to ecological and/or environmental problems
• A demonstrated experience of collecting, integrating, and visualizing various types of spatial and temporal data
• A demonstrated capacity to work independently to accomplish statistical modeling and writing tasks
• Experience working on team research projects
Start Date & Duration of Appointment: Start Date & Duration of Appointment: As soon as possible; 1 year with the potential for renewal subject to satisfactory performance and continued funding. |
