The Department of Geomatics Engineering in the Schulich School of Engineering invites applications for a Research Associate in Data Analytics .
This Full-time Fixed Term position is for approximately 4 months (based on length of grant funding), with the possibility of extension.
This position reports to the Principal Investigator, and will conduct scientific analyses of environmental data by applying various techniques ranging from classical statistical methods to machine learning, to understand the temporal and spatial trends, hot spots, patterns and relationships to develop empirically based predictive models based on casual relationships in a variety of environmental data.
The research project is in the discipline of environmental data analytics. The research goal is to analyze the long-term environmental data sets as a whole to identify patterns, trends and hot spots and to establish possible relationships between trends, environmental events and changing ground conditions with an ultimate objective to develop empirically based predictive models.
Summary of Key Responsibilities (job functions include but are not limited to) :
Take responsibility for successful completion of research projects from the conception stage through the submission of all project deliverables
Attend research team meetings, conferences, and seminars to discuss the research methodology and present the research results
Take full responsibility for completing data QA / QC prior to using them in analysis
Compilation and preparation of variety of environmental data from various sources
Environmental data analytics using various techniques including advanced statistical methods, fuzzy logics, artificial neural networks, machine learning etc.
Development of predictive models based on data analytics
Work in collaboration with government and industry partners to understand their perspectives, knowledge gaps and needs
Participate in team meetings with government and industry partners to report progress on the project
Engage with multidisciplinary team members to define technical and functional requirements
Provide presentations at international and national conferences
Prepare periodic progress reports and a final report for submission to the sponsoring agency
Conduct literature review when required to
Establishes and maintains effective, productive relationships with staff, peers, immediate supervisor and senior management and with the campus community
Qualifications / Requirements :
A postgraduate degree (MSc or PhD) in science, mathematics, statistics, engineering, or a closely related field with a focus on data analytics
1-3 years of related technical experience within the research specialty field
Strong knowledge of statistics and advanced experience in programming and analyzing datasets is required
Expertise in advanced statistical and supervised / unsupervised machine learning methods (e.g., time series analysis, clustering techniques, fuzzy logic, and artificial neural network-
deep learning) is required
Technical expertise regarding data models, development, and data mining is required
Knowledge of and experience with programming (Python, Matlab, R, etc.) is preferred
Knowledge of and experience with climate science / data would be an asset
Perform detailed design, documentation, and implementation of moderately complex technological systems
Knowledge of theory and practical application of technical procedures
Must be open to learning and development, and be willing to accept new challenges and assignments
Prove semi-skilled to skilled technical procedures to support the research project
Demonstrated strong communication skills
A positive attitude and the ability to work independently and as part of a team are critical to success
Excellent scientific writing skill
Ability to complete work within a timeframe
Application Deadline : August 16, 2019
We would like to thank all applicants in advance for submitting their resumes. Please note, only those candidates chosen to continue on through the selection process will be contacted.
This position is part of the AUPE bargaining unit, and falls under the Technical Job Family, Phase 1 .