POSTDOCTORAL POSITION IN SPATIAL DATA MINING EXPLORING CO-LOCATION OF ADVERSE BIRTH OUTCOMES AND ENVIRONMENTAL VARIABLES. (Dr. Osmar Zaiane, Department of Computing Science, Dr. Alvaro Osornio Vargas, Department of Pediatrics, Project: DOMINO (Data Mining and Newborn Outcome Project)
This position is full-time (including benefits) for one year and renewable for a second year. 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 an interdisciplinary 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. This interdisciplinary research project investigates chemical and socioeconomic environmental influences on maternal/infant birth outcomes (low birth weight, preterm births, and stillbirths). 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. 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 Killam Annual Professorships
How To Apply:
Forward your CV (including a list of publications and description of any previous research), motivation letter and contact information for three references (two letters of recommendation) to:
Dr. Osmar Zaiane and Dr. Alvaro Osornio Vargas.
In subject field indicate DOMINO: Post-Doc Spatial Data Mining position.
Email address: zaiane@cs.ualberta.ca and osornio@ualbetra.ca
Closing date: Applications will be reviewed immediately and the position will remain open until filled.
We thank all applicants for their interest; however, only those individuals selected for an interview will be contacted.
The University of Alberta offers appointments on the basis of merit. We are committed to the principle of equity in employment. We welcome diversity and encourage applications from all qualified women and men, including persons with disabilities, members of visible minorities and Aboriginal persons.