QuantOmics

Applications Now Open: Join the QuantOmics Inaugural Cohort

Fri, 15 Aug 2025 09:00:00 +0000

QuantOmics is now accepting applications for its inaugural cohort of graduate trainees and postdoctoral fellows. This is a rare opportunity to join a nationally unique, interdisciplinary training program at the intersection of quantum biosensing, computational genomics, and artificial intelligence.

What You’ll Gain

As a QuantOmics trainee, you will:

Develop deep expertise across three interconnected domains — quantum sensing, genomics, and AI — building a skill set that no traditional graduate program can offer
Be supported by a Mentorship Trio consisting of your primary supervisor, a co-supervisor from a complementary stream and institution, and an industry advisor
Complete a mandatory mobility placement (6–8 weeks) in either an academic lab or industry R&D environment
Participate in the Multimodal-Omics Data Integration Bootcamp — a three-week hands-on intensive building complete sensor-to-insight pipelines
Access industry internship opportunities with partners including Epiloid Biotech, C2MI, Terry Fox Research Institute, Ontario Genomics, Klick Health, Alimentiv, and Novavax
Present your research at the QuantOmics Symposium with a concurrent job fair
Receive a QuantOmics supplement on top of your institutional stipend to support program activities and mobility

Who Should Apply?

We welcome applicants from a wide range of disciplines with no prior cross-domain experience required:

Background	Likely Entry Stream
Electrical Engineering, Physics, Applied Physics	Stream 1 (Quantum Biosensing)
Computer Science, Biomedical Engineering	Stream 3 (AI Therapeutics)
Computational Biology, Bioinformatics	Stream 2 (Genomics)
Molecular Genetics, Genomic Medicine	Stream 2 (Genomics)
Health Sciences (with quantitative training)	Stream 2 or 3

How to Apply

Find your supervisor — Browse the research team and identify a faculty member whose research resonates with you. Email them directly to discuss potential supervision.
Apply to the university — Submit your application through the graduate admissions portal of your chosen partner institution.
Note your QuantOmics interest — Clearly state your interest in the QuantOmics CREATE program in your application.
Your supervisor nominates you — Once admitted, your supervisor nominates you to the QuantOmics Steering Committee for formal enrollment.

Partner institutions: Toronto Metropolitan University · McGill University · Queen’s University · Université Laval · University of Saskatchewan · University of Toronto

Our Commitment to Equity & Inclusion

QuantOmics is committed to building a diverse cohort. We actively recruit from communities underrepresented in STEM, including women and non-binary individuals in engineering and AI, Black and Indigenous researchers, and candidates from all geographic backgrounds across Canada.

Job ads use unbiased language and are circulated through targeted networks including the Black Professionals in Tech Network, Native Women’s Association of Canada, Society for Canadian Women in Science & Technology, Women in Genomics, and Indigenous Student Services offices.

Questions?

Contact the QuantOmics Program Coordinator at n77khan@torontomu.ca or visit the Apply page for full details.

We look forward to welcoming our founding cohort of QuantOmics trainees.

QuantOmics Receives NSERC CREATE Funding to Train Canada's Quantum-Biomedical Leaders

Sun, 01 Jun 2025 09:00:00 +0000

We are thrilled to announce that the NSERC CREATE in AI-Driven Quantum Sensing and Genomics for Precision Therapeutics (QuantOmics) has received funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) to establish Canada’s first integrated research and training pipeline bridging quantum nanotechnology, computational genomics, and artificial intelligence.

What is QuantOmics?

QuantOmics is designed to dismantle disciplinary and training silos that currently prevent Canada from fully realizing the potential of quantum-enabled precision medicine. Our program brings together a consortium of six research-intensive universities — Toronto Metropolitan University, McGill, Queen’s, Université Laval, University of Saskatchewan, and University of Toronto — along with a cross-sectoral network of eight industry and clinical partners.

Over six years, QuantOmics will train 93 highly qualified personnel (HQP), including 36 undergraduates, 36 Master’s students, 15 PhD candidates, and 6 Postdoctoral Fellows, producing 170 one-year training units.

The Vision

The next frontier in medicine lies in translating an individual’s unique genomic blueprint into personalized, life-saving therapeutics. Realizing this vision requires overcoming a fundamental measurement barrier: today’s biological assays lack the sensitivity to detect the ultra-low-abundance biomarkers that signal disease onset.

QuantOmics addresses this with an integrated three-stream research pipeline:

Stream 1 — Quantum Probe Design & Fabrication: Engineering biosensors capable of detecting biomarkers at the attomolar level
Stream 2 — Genomics Signal Integration: Converting quantum sensor data into high-fidelity multi-omic datasets
Stream 3 — AI-Powered Therapeutic Design: Building predictive models to guide rational therapeutic and vaccine design

A New Kind of Training

Every QuantOmics graduate trainee is supported by a “Translational Trajectory” Mentorship Trio — pairing them with a primary academic supervisor, a co-supervisor from a complementary stream at a different institution, and an industry advisor. This structure ensures trainees develop deep interdisciplinary literacy and strong professional networks from day one.

The program includes five specialized courses (three on-demand, one immersive bootcamp, and one responsible innovation seminar series), industry and academic mobility placements, annual team hackathons, and triennial national symposiums with concurrent job fairs.

Quote from the Program Director

“QuantOmics is not just a training program — it’s a paradigm shift in how we prepare the next generation of Canadian researchers. A QuantOmics graduate will speak the language of hardware engineers, molecular biologists, and AI developers simultaneously. That kind of integrative thinking is exactly what Canada needs to lead in the global quantum-biomedical economy.” — Dr. Naimul Khan, Program Director, QuantOmics

Applications Opening Soon

The QuantOmics program will begin accepting applications for its inaugural cohort. Prospective trainees at all levels — undergraduate, Master’s, PhD, and Postdoctoral — are encouraged to review the program details and reach out to faculty whose research aligns with their interests.

Applications are made through the standard graduate admissions process at any of the six partner universities, with supervisor nomination to the QuantOmics program.

Learn more about how to apply →

This research is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Collaborative Research and Training Experience (CREATE) program.

Stream 1: Quantum Probe Design & Fabrication

Wed, 01 Jan 2025 00:00:00 +0000

Overview

Stream 1 is the hardware engine of the QuantOmics pipeline. Trainees in this stream design and fabricate next-generation quantum biosensor probes that achieve detection sensitivity at the attomolar level — capturing the decisive molecular events that trigger biological cascades long before conventional sensors can detect them.

The core insight: biological systems exhibit immense signal amplification, where just a few molecules can trigger a cascade of events. Stream 1 builds the measurement tools that can capture these initial molecular events. This enables the entire downstream pipeline — genomic analysis and AI-guided therapeutic design — to operate on signals of unprecedented clarity.

Research Focus Areas

Nitrogen-Vacancy (NV) Center Diamond Probes

NV centers in diamond are atomic-scale quantum sensors with extraordinary sensitivity to magnetic fields, temperature, and single molecules. Trainees work on:

Fabrication and functionalization of NV-center diamond nanoparticles for biological labeling
Optically detected magnetic resonance (ODMR) signal readout
Integration with microfluidic delivery systems for single-cell sensing

Quantum Dot Synthesis & Photonic Sensing

Semiconductor quantum dots offer tunable optical properties for highly sensitive biosensing. Research projects include:

Synthesis of biocompatible quantum dots (CdSe, InP, carbon-based) for specific molecular targeting
Photonic crystal resonator integration for signal amplification
Multiplexed detection platforms for simultaneous multi-analyte sensing

CMOS/MEMS Integrated Sensor Systems

Miniaturized, integrated sensor platforms enable practical, deployable biosensors. Trainees engage in:

Design of CMOS read-out circuitry for quantum sensor arrays
MEMS-based microfluidic integration for sample handling
Implantable and injectable wireless biosensor networks for real-time monitoring
Energy-efficient signal conditioning electronics

Spin-Based Magnetometry

Ultra-sensitive detection of magnetic signatures from biological processes:

Spin-based detection of neurotransmitters and cellular signaling molecules
Quantum-enhanced noise-limited detection strategies
Integration with organ-on-a-chip platforms for in vitro validation

Validation Platform

A cornerstone of Stream 1 methodology is the use of patient-derived organoid and organ-on-a-chip platforms. These physiologically relevant 3D microenvironments mimic human tissue far more accurately than traditional 2D cell cultures. Trainees validate their quantum sensors against these biological systems — in partnership with Stream 2 biologists — to confirm performance in contexts that predict human clinical response.

Partner organizations C2MI and Epiloid Biotech provide access to quantum fabrication infrastructure and organoid setups that are otherwise inaccessible to most Canadian universities.

Example Trainee Projects

NV-center nanosensor for attomolar cytokine detection — fabricating and characterizing a diamond NV probe functionalized for IL-6 detection in organoid supernatant
Quantum dot multiplexed assay for cancer biomarkers — designing a photonic chip that simultaneously detects three circulating tumor DNA fragments
Integrated CMOS biosensor for real-time cardiotoxicity screening — miniaturized sensor array for monitoring cardiomyocyte electrical activity in organ-on-chip models
Wireless implantable quantum magnetometer — injectable sensor for continuous in vivo monitoring of immune cell activity

Stream 1 Co-Leads

Dr. Virgilio Valente (TMU) — CMOS/MEMS integrated sensors, wireless biosensor networks
Dr. Shayan Rayan (USask) — Quantum nanotechnology, nanopore integration
Dr. Harry Ruda (UofT) — Photonic sensors, semiconductor nanomaterials (Stanley Meek Chair in Advanced Nanotechnology)
Dr. Sara Mahshid (McGill) — Microfluidics, lab-on-chip biosensing
Dr. Stefania Impellizzeri (TMU) — Quantum dot synthesis, nanomaterial chemistry (Jet Ice Research Chair)

Courses Supporting Stream 1

Course 1.1 — Biosensor Engineering for Precision Health
Course 1.2 — Quantum Nanotechnology for Life Sciences
Bootcamp 1.4 — Multimodal-Omics Data Integration (connecting sensor output to genomic pipelines)

Stream 2: Genomics Signal Integration

Wed, 01 Jan 2025 00:00:00 +0000

Overview

Stream 2 is the data backbone of the QuantOmics pipeline. Trainees in this stream tackle the grand challenge of converting noisy, high-dimensional quantum sensor data into high-fidelity, analytically tractable multi-omic datasets. Without Stream 2, the hardware signals from Stream 1 cannot be interpreted, and the AI models of Stream 3 have no validated data to learn from.

The stream bridges two worlds: the messy, real-world signals from quantum probes operating in complex biological matrices, and the clean, structured representations required by state-of-the-art computational genomics and AI methods.

Research Focus Areas

Whole-Genome Sequencing & Variant Analysis

Long- and short-read sequencing data generated by quantum-enhanced nanopore platforms requires sophisticated analysis. Trainees work on:

Applying comprehensive whole-genome sequencing (WGS) analyses to brain organoids to identify therapeutic targets for neurodevelopmental disorders such as autism
Developing improved variant calling algorithms that account for unique noise characteristics of quantum-coupled sequencing
Building tools for structural variant detection in complex genomic regions

Multi-Omic Data Fusion

Integrating data across molecular scales — from DNA to protein to cellular phenotype. Key projects include:

Fusing electrical and optical sensor data streams with nanopore sequencing reads and methylation maps
Building end-to-end Snakemake/Nextflow pipelines for reproducible multi-omic analysis
Developing early vs. late fusion strategies for genomic, transcriptomic, proteomic, and EHR data
Applying representation learning to extract shared latent features across data modalities

Computational Tools for Sensor-Derived Genomic Data

Novel sensors generate novel data formats. Stream 2 trainees develop new bioinformatics tools:

Signal processing algorithms for denoising raw quantum sensor output before genomic analysis
Probabilistic models for base-calling from quantum-coupled nanopore reads
Quality control frameworks tailored to attomolar-sensitivity assay data

Epigenomics & Methylation Analysis

DNA methylation patterns hold rich information about cell state and disease. Trainees build:

Pipelines for integrating methylation data with quantum sensor readout from epigenetic biosensors
Differential methylation analysis in disease-relevant organoid models
Tools for cross-referencing methylation signatures with EHR phenotype data

Validation Against Real Disease Models

Stream 2 research is validated against clinically relevant biological systems, primarily using patient-derived organoids developed in collaboration with Stream 1. Key application areas:

Neurodevelopmental disorders — applying WGS to brain organoids to identify new therapeutic targets for autism, building on existing team expertise in the genomic architecture of these conditions
Cancer — integrating multi-omic data to characterize tumor heterogeneity and identify drug-resistant cell populations in organoid cultures
Cardiotoxicity — developing computational pipelines for analyzing multi-omic data from cardiomyocyte organ-on-chip models under drug perturbation

Example Trainee Projects

Whole-genome sequencing of autism brain organoids — identifying novel de novo variants and gene regulatory disruptions associated with autism spectrum disorder
Snakemake pipeline for multi-omic fusion — building a reproducible, containerized pipeline integrating nanopore sequencing, ATAC-seq, and EHR metadata
Methylation-guided biomarker discovery — developing an algorithm that identifies disease-specific methylation patterns detectable by quantum epigenetic biosensors
Cross-ancestry variant classification — building genomic models that integrate population-diverse genetic marker data to reduce bias in variant pathogenicity prediction

Equity & Diversity in Genomic Data

A critical challenge in Stream 2 is the profound lack of diversity in genomic reference databases, which are overwhelmingly of European origin. Trainees in this stream are explicitly trained to:

Integrate data representing comprehensive genetic markers from diverse populations
Understand how racial bias in genomic AI can lead to misclassification for underrepresented groups
Apply ethical frameworks for Indigenous data sovereignty and community consent in genomic research
Validate tools across diverse cell lines to ensure equitable performance

Stream 2 Co-Leads

Dr. Brett Trost (UofT / SickKids) — Computational genomics, multi-omic pipeline development
Dr. Jacques Corbeil (U Laval / MILA) — Medical genomics, AI-driven data integration, organ-on-chip resources
Dr. Brenda Andrews (UofT) — Functional genomics, systems biology, gene networks (Tier 1 CRC)

Courses Supporting Stream 2

Course 1.3 — AI in Genomics
Bootcamp 1.4 — Multimodal-Omics Data Integration (the core course for this stream)
Course 1.5 — Responsible Innovation & EDI in Precision Health (Indigenous data sovereignty, bias in genomic AI)

Stream 3: AI-Powered Therapeutic Design

Wed, 01 Jan 2025 00:00:00 +0000

Overview

Stream 3 closes the QuantOmics pipeline. Trainees here build the predictive AI models that translate the high-fidelity multi-omic datasets generated by Streams 1 and 2 into actionable therapeutic insights — identifying immunogenic targets, predicting treatment response, and guiding the rational design of personalized vaccines and precision therapeutics.

This stream operates at the intersection of machine learning, structural biology, and clinical translation. It is where the quantum-to-clinic vision of QuantOmics is most directly realized.

Research Focus Areas

Neoantigen Prediction & Vaccine Design

Personalized cancer vaccines require identifying tumor-specific neoantigens — mutated peptides that the immune system can be trained to recognize. Trainees work on:

Benchmarking and adapting next-generation pathogenicity predictors to identify immunogenic neoantigens for personalized vaccine design
Developing multimodal fusion models that integrate genomic variant data with structural protein features and HLA binding affinity predictions
Building end-to-end vaccine design pipelines from quantum-enhanced sequencing data to candidate antigen ranking

Generative AI for Drug Discovery

Using generative models to explore vast chemical spaces and identify novel therapeutic candidates:

Applying generative AI models (VAEs, diffusion models) for anomaly detection to identify drug-resistant cells in organoid cultures
Designing latent-space representations of molecular structures conditioned on multi-omic signatures
Generating and evaluating novel small molecule candidates for precision targeting

Multimodal Fusion for Clinical Diagnostics

Combining signals across modalities for robust clinical prediction:

Creating deep learning frameworks for the multimodal fusion of electrical, optical, and genomic sensor data for cardiotoxicity screening
Developing transformer-based models for multimodal representation learning across genomic, imaging, and clinical data
Building uncertainty-aware prediction models for clinical deployment in high-stakes settings

Immune Cell Profiling & Treatment Efficacy

Quantifying the immune response is critical to evaluating therapeutic efficacy:

Using advanced loss functions and segmentation models to precisely identify and quantify immune cell infiltration in microscopy images
Providing a direct, AI-based measure of therapeutic efficacy in organoid and in vitro models
Developing self-supervised AI models to automatically classify cellular phenotypes from high-content imaging data, enabling large-scale unbiased analysis

Trustworthy & Responsible Clinical AI

AI deployed in clinical settings must be fair, interpretable, and robust:

Developing explainability frameworks (SHAP, integrated gradients, concept-based explanations) for genomic AI models
Quantifying and mitigating performance disparities across patient demographic groups
Building validation frameworks for quantum-AI systems navigating Health Canada regulatory pathways

Example Trainee Projects

Personalized neoantigen vaccine pipeline — an end-to-end system from quantum-enhanced WGS of tumor organoids to a ranked list of candidate immunogenic peptides
Generative molecular design for drug resistance — applying a diffusion model to identify novel inhibitor candidates for drug-resistant cancer cells detected in organoid assays
Multimodal cardiotoxicity classifier — a fusion model combining quantum sensor electrical signals, microscopy images, and gene expression data to predict drug-induced cardiac toxicity
Immune infiltration quantification tool — a deep learning segmentation system deployed on multiplexed fluorescence images of treated organoids to measure therapeutic response
Bias audit framework for genomic AI — a systematic evaluation tool that measures and corrects performance disparities of genomic AI models across ancestrally diverse patient populations

The “Personalized Vaccine Design Challenge”

Stream 3 forms the intellectual core of QuantOmics’ signature annual Personalized Vaccine Design Challenge — a biennial team hackathon where interdisciplinary trainee teams tackle the complete sensor-to-vaccine pipeline, culminating in a presentation to a panel of academic, clinical, and industry judges. This challenge uniquely forces integration across all three streams.

Equity & Responsible AI in Therapeutic Design

QuantOmics explicitly trains Stream 3 trainees to address AI ethics in clinical applications. Key training elements include:

Course 1.5 — Responsible Innovation & EDI in Precision Health: regulatory pathways, inclusive AI design, Indigenous data sovereignty
Frameworks for ethical AI in oncology and emerging quantum technologies
Proactive identification and correction of demographic biases at the model design stage, not as an afterthought

Stream 3 Co-Leads

Dr. Naimul Khan (TMU) — Multimodal ML, AI in healthcare, biosensing (Program Director)
Dr. Amber Simpson (Queen’s) — Biomedical computing, cancer informatics, AI for clinical data (Tier 2 CRC)

Contributing Faculty:

Dr. Brenda Andrews (UofT) — Systems genetics, functional genomics (Tier 1 CRC)
Dr. Jacques Corbeil (U Laval / MILA) — Genomic AI, multi-omics

Courses Supporting Stream 3

Course 1.3 — AI in Genomics (primary course for this stream)
Bootcamp 1.4 — Multimodal-Omics Data Integration
Course 1.5 — Responsible Innovation & EDI in Precision Health