Staff Research Scientist (Machine Learning Specialist)

University of Toronto; Toronto; On-site; CAD 53,000 - 101,000; Full time

Job summary

A prestigious educational institution in Toronto is seeking a motivated Staff Research Scientist to advance AI-driven autonomous discovery in the field of human organ mimicry. The candidate will leverage machine learning and computational biology expertise to develop novel workflows and collaborate with a diverse team of experts. This is a two-year term position with competitive salary and potential for renewal.

Qualifications

• 1 to 5 years in R&D for ML methods in biological data analysis.
• Experience managing AI/ML projects and lab partnerships.
• Strong presentation and publication record.

Responsibilities

• Develop ML algorithms for monitoring in vitro models.
• Design ML pipelines for large-scale imaging.
• Manage R&D projects for industry partners.

Skills

Machine learning

Computational biology

Programming in Python

High-performance computing

Data analysis

Education

Ph.D. in computational biology or related field

Tools

Python

MATLAB

C++

CUDA

SQL

Job description

Overview

Staff Research Scientist (Machine Learning Specialist) - SDL6

Date Posted: 09/02/2025
Req ID: 45033
Faculty/Division: Faculty of Arts & Science
Department: Acceleration Consortium
Campus: St. George (Downtown Toronto)

Description:

The Acceleration Consortium (AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of academia, industry, and government that leverages the power of artificial intelligence (AI), robotics, materials sciences, and high-throughput chemistry to create self-driving laboratories (SDLs), also called materials acceleration platforms (MAPs). These autonomous labs rapidly design materials and molecules needed for a sustainable, healthy, and resilient future, with applications ranging from renewable energy and consumer electronics to drugs. AC Staff Research Scientists will advance the field of AI-driven autonomous discovery and develop the materials and molecules required to address society’s largest challenges, such as climate change, water pollution, and future pandemics.

The Acceleration Consortium promotes an inclusive research environment and supports the EDI priorities of the unit.

The AC received a $200M Canadian First Research Excellence Grant for seven years to develop self-driving labs for chemistry and materials, the largest ever grant to a Canadian University. This grant will provide seven years of funding to execute its vision.

The AC is developing seven advanced SDLs plus an AI and Automation lab:

• SDL1 - Inorganic solid-state compounds for advanced materials and energy
• SDL2 - Organic small molecules for sustainability and health
• SDL3 - Medicinal chemistry for improving small molecule drug candidates
• SDL4 - Polymers for materials science and biological applications
• SDL5 - Formulations for pharmaceuticals, consumer products, and coatings
• SDL6 - Biocompatibility with organoids / organ-on-a-chip
• SDL7 - Synthetic scale-up of materials and molecules (University of British Columbia partner lab)
• A central AI and Automation lab to support all the SDLs

Position Overview:

We are seeking a motivated and skilled researcher to join the Acceleration Consortium working with the Human Organ Mimicry SDL. The Self-Driving Lab (SDL) focused on Human Organ Mimicry (HOM) embodies an autonomous AI-assisted platform for culturing and screening high-fidelity models of functional tissues and diseases. In addition to fundamental capabilities like cell passaging and sample preparation, the platform will facilitate closed-loop optimization campaigns designed to optimize cell culture conditions, automated generation of model-specific datasets and production of highly reproducible batches of cells with specific phenotypes, and development of advanced automated workflows and AI tools (e.g., organ-on-a-chip models, automated workflows, and AI decision support).

The ideal candidate should have strong expertise in machine learning (ML) and computational biology, with the capability to apply those skills toward imaging data (e.g., live cell microscopy). The successful candidate will contribute to advancing ML-driven analysis of high-content imaging data to achieve 1) better OOC tissue model functional evaluation and clinical benchmarking, 2) optimization of cost-efficient workflows and reproducibility. Knowledge of current ML models, their strengths and weaknesses, and strategies for (hyper)parameter optimization is required. Prior use of Bayesian optimization or other active learning methods is preferred. Strong coding skills in Python or a comparable language are expected, with the ability to develop analysis pipelines and tools suitable for publication-quality research. The role involves developing novel computational approaches for biological discovery and collaborating in an interdisciplinary environment. Additional expertise related to biological knowledge of wet lab experimentation for imaging data is a beneficial bonus.

This posted position is for a Staff Research Scientist at SDL6 (Human Organ Mimicry).

This is a two-year term with the possibility of renewal.

Expertise that is desired:

Computational expertise

• Life science and physical science applications of machine learning in biology, bioengineering or molecular biology or related fields
• Programming and high-performance computing
• Experience in design of computational pipelines for large-scale imaging
• Experience with programming languages and scripting methods (Python, MATLAB, C++, CUDA, Bash, SQL) and ML/deep learning methods
• Active learning, exploration, optimal experiment design, Bayesian optimization, reinforcement learning, and/or representation learning
• Experience in development and application of ML/deep learning methods for high throughput cell imaging data

Additional expertise that is desired (not required):

• Experience with ML-based tools for image-analysis and signal processing, development of ML prediction tools
• Experience with PyTorch and/or TensorFlow, databases and high-content imaging platforms
• Familiarity with generative modeling
• Experience with advanced ML techniques for representation learning and multi-modal data integration

The Staff Research Scientist will work with a diverse team of leading experts at U of T and with Staff Scientists in Human Organ Mimicry and AI SDL.

The Staff Research Scientists involved in the AC are highly skilled researchers who will work independently to develop AI and automation technologies required to build robust SDLs, manage these SDLs, and design research programs that leverage the SDL platforms to discover materials and molecules. They will work collaboratively, sharing knowledge among each other, faculty, and trainees, and report to the Academic Director and Executive Director of the AC.

The components and duties of the work include:

• Machine learning for SDL Development

Working with the AC community to align computational methods with experimental workflows, developing ML algorithms for monitoring in vitro cell culture models and enabling data-driven autonomous experimentation. Develop computational tools for the analysis of high-resolution microscopy images to support quality control and autonomous decision-making.

• SDL and Automation Development

Determine SDL capabilities, design and test closed-loop campaigns for cell culture media optimization, develop SDL plans to meet user requirements, and design novel instruments and software for autonomous cell culture experiments. Select, procure, and install equipment required for SDLs.

• Research Direction

Develop independent research programs leveraging the AC SDLs, translating computational approaches from 2D to 3D systems (organoids, OOCs), publish high-quality manuscripts, and contribute to grant writing.

Tasks include:

• Manage R&D projects for AC’s industry partners
• Develop plans supporting collaborations and resource estimation
• Work with Product Managers to align outcomes with partner requirements
• Present research at conferences and promote AC’s research capacity
• Prepare and submit proposals to granting agencies and report progress
• Prepare manuscripts for peer-reviewed journals and manage the publication process
• Mentor junior lab members and foster collaboration

Other

• Support consulting related to SDL applications for partner materials discovery
• Support research-focused events such as Annual Symposium

MINIMUM QUALIFICATIONS

Education – Ph.D. in computational biology, bioinformatics, biophysics, biomedical engineering, computer science, or related field.

Experience

• 1 to 5 years in accelerated R&D for ML/deep learning methods in biological or chemical data analysis
• Experience managing a major AI/ML research project, collaboration with industry partners, and PI-level responsibilities
• Experience overseeing a lab and working with industry partners
• Strong record of presenting at academic conferences
• Demonstrated scholarly excellence and publication record

Skills

• Electronic/hardware-oriented programming and ML
• Strong written and oral English communication
• Collegiality and ability to work independently

Other

• Proven ability to write and prepare manuscripts, presentations, and abstracts for peer-reviewed journals

All qualified candidates are encouraged to apply; Canadians and permanent residents will be given priority

Closing Date: 10/31/2025, 11:59PM ET
Employee Group: Research Associate
Appointment Type: Grant - Term (2 years, possible renewal)
Schedule: Full-Time
Pay Scale Group & Hiring Zone: R01 -- Research Associates (Limited Term): $53,520 - $100,350 (salary may exceed range based on skills/experience)
Job Category: Research Administration & Teaching

Diversity Statement

The University of Toronto embraces Diversity and is building a culture of belonging. We strongly encourage applications from Indigenous Peoples, Black and racialized persons, women, persons with disabilities, and people of diverse sexual and gender identities. We value applicants who have demonstrated a commitment to equity, diversity and inclusion and recognize that diverse perspectives, experiences, and expertise are essential to strengthening our academic mission.

Accessibility and Contacts

The University strives to be an equitable and inclusive community and provides accommodations if needed. If you require accommodations during the application process, please contact [e-mail].