Postdoctoral Researcher
- Toronto, ON, Canada
Full-time
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Job details
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- Full-time
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- Posting language
- English
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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.
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
Categories
- Science & Research
- Technology
- Healthcare
- Data & Analytics
- Software
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