Postdoctoral Researcher

McGill University – IVADO Postdoctoral Researcher – Postdoctoral Researcher

Position Title Postdoctoral Researcher

Company Information

Before applying, please note that to work at McGill University, you must be both authorized to work in Canada and willing to work in the province of Quebec at the campus where the position is based / located. Any offer to non-Canadian citizens/non-permanent residents is contingent on the candidate obtaining a work permit after an offer has been made and a letter of invitation sent.

McGill University is an English-language university where most teaching and research activities are conducted in the English language, thereby requiring English communication both verbally and in writing.

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, accessibility View email address on apply.j-vers.com.

Duties and Responsibilities

The applicant will jointly apply IVADO Postdoctoral Research Funding with the PI and the application result will be announced early March 2027.

Funding start date: between April 1 and September 1, 2027

The postdoctoral researcher will be supervised by Prof. Archer Yang ( who is seeking a highly motivated postdoc to join their research groups at McGill University. The successful candidate will focus on the fundamental theory and methodology of uncertainty quantification (UQ) for machine learning, addressing critical statistical problems in modern AI. Accelerating drug discovery requires navigating vast and complex chemical spaces where predictive models without calibrated uncertainty are insufficient for optimal decision-making. This role focuses on bridging rigorous statistical theory with state-of-the-art machine learning to provide mathematically sound UQ for AI models deployed in real-world biochemical settings. Fundamentally, the core mandate of this position is the rigorous development of novel statistical methodology for uncertainty quantification. Complementing this theoretical focus is an additional, highly attractive opportunity to collaborate directly with the Eli Lilly research lab. This industrial connection provides an invaluable secondary avenue to translate and evaluate these methodological innovations against pressing, real-world challenges in pharmaceutical research.

• Methodological Development: Formulate and mathematically analyze novel statistical frameworks for uncertainty quantification, robust inference, and reliable decision-making in deep learning and AI.
• Applied Research Opportunity: Implement and scale these methods to tackle real-world drug discovery problems, working closely with domain experts.
• Industrial Collaboration: While the primary focus of this role remains the rigorous development of foundational statistical methodology for uncertainty quantification, the candidate will have the additional opportunity to engage with scientists at the Eli Lilly research lab. This collaboration provides a direct conduit to translate and validate fundamental theoretical advances on real-world pharmaceutical applications.
• Publication: Prepare and submit high-impact manuscripts. The candidate is expected to target top-tier statistical and biostatistical journals as well as top machine learning and AI conferences.

Hours per week: 35 (Full time)

Position Qualifications

• A Ph.D. in Statistics, Biostatistics, Computer Science, Applied Mathematics, or a closely related quantitative discipline (completed or near completion).
• Strong foundational knowledge in statistical machine learning, high-dimensional inference, and/or uncertainty quantification.
• Demonstrated computational proficiency (e.g., Python, R) for both statistical simulations and scaling ML algorithms.
• An outstanding track record of publications in top-tier statistics journals or premier AI/ML conferences.
• Preferred but not required: Previous exposure to computational biology, cheminformatics, or drug discovery is an asset, but exceptional candidates with a purely methodological statistical/ML background are also highly encouraged to apply.

Salary Range An annual salary of $80,000, including benefits. The total funding period is two years, conditional to the submission of a report at the end of the first year.

Benefits will align with postdoctoral policies and collective agreement at McGill University.