Studentship In Computer Science

The School of Computer Science, University of Windsor, is inviting application for fully funded MSc. or Ph.D. studentships in the field of Computer Science. The successful candidate will be working with a multidisciplinary group whose research falls within the disciplines of artificial Intelligence, chaos theory, and health informatics. The successful candidate(s) will be supervised Dr. Robin Gras. The project is funded through multiple NSERC grants. They are looking for a talented and highly motivated individual to implement and extend the P&H method for measuring chaos level. This method is a new and efficient method for detecting the random signals from deterministic signal and it is based on the Poincaré section and the Higuchi fractal dimension. This method can be used to detect chaotic behaviour in a signal. This method recently has been applied to some biomedical, ecological and financial data. A method, GenericPred, has been developed based on the P&H measure to predict the future states of complex non-linear time series. This method provides a first step towards accurate and comprehensive time series long-term predictions. Although with respect to long-term predictions it is impossible to predict the exact values, GenericPred’s performance shows great potential for predicting the time series’ trend. The successful candidate will extend the method to make the computation of the P&H measure more generic. Optimization methods will be applied and analysis of the behavior of the measure on many different conditions will be performed. New applications of the predictive methods will be developed in particular for multiple diseases diagnosis.

Successful candidates will receive an annual stipend of $12,000 CAD/year for Ph.D. student and $6,000 for MSc. student. Students will also be required to apply for other scholarship opportunities (NSERC, OGS, etc.) to increase their stipends.

Eligibility:

• The candidate must have a strong background in chaos theory and probability and statistics with preference given to candidates that have published in top conference proceedings and journals in these fields. In addition preference will be given to candidates that have previous experience in machine learning and optimization.
• All candidates will be expected to present his/her work in multiple ways (i.e. conference presentations, posters, proceedings, and journal articles), therefore strong verbal and written skills in English are required. We also are looking for someone who can work well with others and share ideas on a regular basis. Candidates must have a Master’s degree in Computer Science
• For the Ph.D. Position, however exceptionally strong candidates with a Bachelor’s degree will be considered as candidates for a Master’s degree with the expectation that the student be fast tracked to a Ph.D. after a year of study.