Postdoctoral Researcher

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A Group C (Unionized) Postdoctoral Researcher is a Research Fellow recognized by the Quebec government employed at McGill with a valid Doctoral degree (PhD or MD) for a maximum of 5 years after obtaining their Doctoral Degree. The incumbent is unionized if more than 25% of his salary is paid by McGill Research funds. Group C Unionized hold a dual status as both students and McGill employees, and are members of the AMURE (Postdocs) bargaining unit. Their pay, benefits, working conditions, etc. are governed by their collective agreement.

Project description:

Uncertainty in machine learning based prediction algorithms is a growing concern, especially in health science applications. This project will explore the possibility of using Bayesian deep learning models to quantify uncertainty levels and compare them with human uncertainty levels in predicting oral health related outcomes. Uncertainty quantification in deep learning is rapidly growing sub-field and this position will offer the opportunity to work with our diverse team of researchers, including our international collaborators for applying latest developments in the field. This project offers great potential for you to publish, both methodological papers within the statistics/machine learning community as well as applied papers in health sciences.

Dentistry further offers a wide array of opportunities of applied machine learning projects with decades of robust digital data availability.  

Position Summary

• Development of data preparation pipelines

• Develop and implement Bayesian deep learning algorithm for different machine learning based prediction models

• Design and analyses simulation-based studies

• Prepare and publish manuscripts

• Support the supervisor in teaching and training students in machine learning techniques

Minimum Qualifications

• PhD in Statistics, mathematics, data science or related field, with a focus in deep learning, completed within the past five years or will have completed all PhD requirements by commencement of appointment.

• Knowledge and experience in Bayesian methods and computing, ideally in health science applications (variational inference, MCMC methods, etc.)

• Familiarity with image processing and classification using deep learning or active learning techniques

• Demonstrated ability to work collaboratively in an applied health research team

Required Skills:

• Statistical programming in R or Python

• Probabilistic programming in Stan or TensorFlow probability or Pytorch (Pyro)

• A track record of relevant publications at top machine learning or computer vision conferences (NIPS, ICML, UAI, JMLR, CVPR, ICCV, PAMI, IJCV, IEEE IT) and/or top-ranked image processing or health research journals is essential.

Desired skills:

• Uncertainty quantification in deep learning for computer vision in health research

• Familiarity working with HPC environments, ideally Compute Canada resources

• A firm grasp of approximate Bayesian machine learning and/or advanced (medical) image processing is a plus.

Faculty/Unit:

Faculty of Dental Medicine and Oral Health Sciences

Location: 2001 McGill College Ave, Montreal, QC, H3A 1G1

Annual Salary:

(AMURE Post-Doctoral Fellow Researcher) $34,611.00

Hours per Week:

35 (Full time)

Location:

McGill College 2001

Supervisor:

Assistant Professor

Position Start Date:

2022-01-14

Position End Date:

2023-01-20

Deadline to Apply:

2022-01-20

McGill University hires on the basis of merit and is strongly committed to equity and diversity within its community. We welcome applications from racialized persons/visible minorities, women, Indigenous persons, persons with disabilities, ethnic minorities, and persons of minority sexual orientations and gender identities, as well as from all qualified candidates with the skills and knowledge to productively engage with diverse communities. McGill implements an employment equity program and encourages members of designated groups to self-identify. Persons with disabilities who anticipate needing accommodations for any part of the application process may contact, in confidence, [email protected] .