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YU
Postdoctoral Researcher
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Toronto, ON
- Full-time

York University

Postdoctoral Researcher

Toronto, ON, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Dec 4, 2026
Posting language: English
Posted: Nov 4, 2026

Original job description

Open Position: Postdoctoral Fellow / Research Engineer

Generative AI, Agentic AI and Genomics

IMPACT-AI Lab, University Health Network and York University, Toronto

The IMPACT-AI Lab at York University invites applications for an exciting Postdoctoral Fellow / Research Engineer opportunity at the intersection of Generative AI, Agentic AI and Genomics. We are seeking a highly motivated candidate to join a collaborative and interdisciplinary research program focused on developing next-generation AI methods for genomic sequence, variant, and multi-omic data, with the goal of advancing discovery in cancer research.

This is an opportunity to contribute to cutting-edge work in a dynamic environment that brings together AI researchers, computational scientists, and clinicians, with strong potential for both methodological innovation and translational impact.

Key Responsibilities

Develop and implement generative AI, foundation models, and agentic AI methods tailored to genomic sequence and variant data
Analyze large-scale genomic and multi-omic datasets
Build robust computational pipelines and support model development, evaluation, and deployment
Collaborate with computational scientists, oncologists, and clinicians to interpret results and validate findings
Contribute to manuscripts, presentations, grant writing, and research dissemination

Qualifications

For Postdoctoral Fellow applicants: PhD in Computer Science, Bioinformatics, Computational Biology, Statistics, or a related field
For Research Engineer applicants: Master’s or PhD in Computer Science, Bioinformatics, Computational Biology, Statistics, or a related field
Strong background in machine learning or deep learning, especially generative methods such as Transformers, VAEs, GANs, or related models
Experience with genomic, multi-omic, or large-scale biological data
Proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow
Demonstrated research or technical experience through publications, projects, open-source contributions, or relevant industry work
Excellent communication skills and ability to work in an interdisciplinary and collaborative environment

Position Details

Start Date: Dec 2026
Duration: Initial 1-year appointment, with possibility of extension and support for career development
Location: Toronto, Ontario (hybrid and remote options available)

How to Apply

Please send the following materials to divya03@yorku.ca

CV including publications and/or relevant technical work
Github link

Application Deadline

Applications will be reviewed on a rolling basis until the position is filled.

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More about this job

Responsibilities

Develop generative AI, foundation model, and agentic AI methods for genomic data, and analyze large-scale genomic and multi-omic datasets. Build computational pipelines, support model development and deployment, collaborate with researchers and clinicians to validate findings, and contribute to research publications, presentations, and grants.

Requirements

Postdoctoral applicants must have a PhD in computer science, bioinformatics, computational biology, statistics, or a related field; research engineer applicants must have a master's or PhD in a related field. Applicants need machine learning or deep learning experience, familiarity with biological data, proficiency in Python and PyTorch or TensorFlow, demonstrated research or technical work, and strong communication and collaboration skills.

Skills

Generative AI
Agentic AI
Machine Learning
Deep Learning
Transformers
Variational Autoencoders
Generative Adversarial Networks
Genomics
Multi-Omic Data Analysis
Python
PyTorch
TensorFlow
Computational Pipelines
Model Evaluation
Interdisciplinary Collaboration

Visa sponsorship

Not detected in the job text

Hiring contact email

divya03@yorku.ca

Keywords

Generative AI
Agentic AI
Foundation Models
Genomics
Genomic Sequences
Variant Data
Multi-Omic Data
Cancer Research
Machine Learning
Deep Learning
Transformers
Variational Autoencoders
Generative Adversarial Networks
Python
PyTorch
TensorFlow
Computational Pipelines
Model Development
Model Evaluation
Model Deployment
Bioinformatics
Computational Biology
Statistics
Clinical Research
Research Dissemination

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