A postdoctoral fellowship in machine learning for high-throughput and/or spatial-temporal data is available at the University of Saskatchewan. Faculty mentors will be Professors Longhai Li
(https://math.usask.ca/~longhai/) and Cindy Feng (https://homepage.usask.ca/~xif794/).
The successful applicant will have a Ph.D. in statistics, or biostatistics or a related field. The incumbent is expected to have strong skills in mathematics, statistical methodologies, statistical
computing, and demonstrated ability of scientific publishing. The research topics will be selected from these areas: machine learning methods for analyzing high-throughput data such as SNP and RNA-seq data, model comparison and checking methods for spatial-temporal
and other correlated data. The application areas will include but not limited to: human and plant genomics, and epidemiology.
The successful candidate will also, if eligible, be nominated for a PIMS postdoc (see https://www.pims.math.ca/scientific/postdoctoral),
which can be held concurrently. As well, postdoctoral fellows in this department are normally given the opportunity to teach up to one course per term. There is also opportunity for the incumbent to work within the newly formed P2IRC (https://p2irc.usask.ca/).
To apply, please submit required documents through mathjob with this link: https://www.mathjobs.org/jobs/jobs/12887