Postdoctoral Fellowships in Machine Learning for Drug Discovery and DEL Data Analysis at Structural Genomics Consortium Structural Genomics Consortium in Canada

The Structural Genomics Consortium (SGC) is advertising postdoctoral openings through the Mitacs Target 2035 Fellows program in Toronto, Canada. These are open-science, AI-enabled drug discovery projects focused on generating and analyzing large-scale data to accelerate proteome-scale ligand discovery.

Opening 1: Postdoctoral Researcher – Machine Learning for Drug Discovery. This project is co-supervised by Matthieu Schapira (SGC / University of Toronto) and Mohammed AlQuraishi (Columbia University). The work centers on machine learning methodologies for drug discovery using large-scale DNA-encoded library (DEL) and affinity selection mass spectrometry (ASMS) datasets. Research topics include representation learning, foundation models, multi-fidelity optimization, binding-site similarity, in-/out-of-distribution prediction, and proteome-scale target prioritization. The role is physically located in the Schapira Lab at the University of Toronto with regular collaboration with Columbia University.

Opening 2: Postdoctoral Researcher – DNA-Encoded Library (DEL) Data Analysis. This project is supervised by Benjamin Haibe-Kains and Matthieu Schapira and is embedded in the University Health Network / SGC research environment in Toronto. The fellow will develop computational pipelines and ML-driven DEL analysis tools, including signal-to-noise optimization, cheminformatics, statistical methodology, pharmacophore models, and virtual screening workflows for ultralarge libraries and de novo design.

Eligibility highlights: applicants should hold a PhD in Computational Chemistry, Computer Science, Bioinformatics, or a related field. Strong experience in machine learning, large-scale data analysis, scientific coding, and interdisciplinary research is emphasized. For the DEL role, prior work with DEL or DNA sequencing data analysis is preferred. For the ML role, familiarity with physics-based molecular modeling is required.

Funding and duration: both positions are full-time postdoctoral fellowships supported by the Mitacs Target 2035 / Mitacs Accelerate Umbrella program. The ML role is initially one year, renewable for up to two additional years; the DEL role is initially two years, renewable for up to two additional years, subject to performance and funding availability. No stipend amount is stated.

How to apply: prepare a CV and cover letter. Apply by email to the contact listed for the relevant project. For the ML role, email [email protected]. For the DEL data analysis role, email [email protected] and use the job title as the subject line.