Post-doctoral position in Spatial Data Mining and Big Data

Location: University of Alberta, Department of Computing Science and Department of Paediatrics

Project: DOMINO (Data Mining and Newborn Outcome Project)

Title: Spatial data mining exploring co-location of adverse birth outcomes and environmental variables.

Abstract: There exists a multitude of available publicly funded databases related to chemical releases by industry, related to neonatal and perinatal health, as well as various statistics and weather and environmental data. We would like to recruit a postdoctoral researcher for one year (or longer) to work with a multidisciplinary team and address problems specific to the integration of these heterogeneous data sources, model the data and apply data mining approaches to discover co-location patterns and other useful hidden patterns in the data.

Context: This interdisciplinary research project investigates chemical and socioeconomic environmental influences on maternal/infant birth outcomes (low birth weight, preterm births, stillbirths and perinatal mortality). The team comprises researchers in computer science, medicine, public health, environmental studies, earth and atmospheric science, etc. Some preliminary work has already been conducted by graduate students from various disciplines on some available data devising specific algorithms to extract patterns. The postdoctoral fellow candidate would consolidate between these works, innovate beyond the preliminary work, assess off the shelf algorithms and tools for patterns discovery on the available spatial data and bridge between the team members.

Description: Spatial data mining is the process of discovering potentially useful patterns from large spatial datasets. Due to the complexity of spatial data, spatial data mining can be more difficult than extracting the same patterns from conventional data, owing to the presence of spatial relationships and autocorrelations. Typical spatial pattern mining tasks include spatial association rule mining, co-location mining, spatial outlier detection, location prediction, etc. Many algorithms exist but each spatial data application has its own idiosyncrasy requiring adaptation or the creation of new algorithms particularly due to the data integration and data modeling dictated by the application. The candidate will be in charge of assessing existing algorithms and discovering new efficient spatial data mining techniques for extracting spatial patterns, and designing pattern visualization tools to help practitioners assess and validate the discovered patterns.

Qualifications:

• A PhD degree in Computer Science on the domain of Data Mining, Data Analytics or Machine Learning.
• An excellent publication record, including papers in high-impact journals and conference proceedings.
• Experience developing and managing research databases
• Expert knowledge of SQL and NoSQL frameworks
• Strong experience in programming languages (such as Java, C++ and Python).
• Experience with data mining tools like Weka
• Experience with spatial data management and mining
• Experience with data integration from disparate data sources.
• Knowledge and experience with data visualization is an asset.
• Good communication skills

Supervision: The project will be formally jointly supervised by Dr. Osmar Zaiane at the Department of Computing Science and Dr. Alvaro Osornio Vargas Department of Pediatrics

Starting date: As soon as possible

Conditions of employment:

• Interested candidates are requested to send an application by e-mail to Dr. Osmar Zaiane (zaiane@cs.ualberta.ca) and Dr. Alvaro Osornio Vargas (osornio@ualbetra.ca) with the subject field: 'DOMINO: Post-Doc Spatial Data Mining position'.
• The application should consist of a motivation letter and a curriculum vitae with a list of publications and description of any previous research. Furthermore, names and contact information for three references are required (with two letters of recommendation).
• Applications will be reviewed immediately and the review process will continue until the position is filled.