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Machine Learning Trainee Jobs in Mississauga, ON

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Machine Learning Trainee information

What kind of projects and tasks can I expect to work on as a Machine Learning Trainee?

As a Machine Learning Trainee, you'll typically assist with data preprocessing, exploratory data analysis, model implementation, and performance evaluation under the guidance of senior data scientists or engineers. You may help clean and organize datasets, experiment with different algorithms, and document your findings. Collaboration is a key part of the role, as you'll often work alongside cross-functional teams, including software developers and business analysts, to support ongoing projects. This hands-on experience provides a strong foundation for advancing to more specialized or independent roles in machine learning.

What is the difference between Machine Learning Trainee vs Data Scientist?

AspectMachine Learning TraineeData Scientist
Required CredentialsBasic understanding of programming, statistics, and machine learning concepts; often pursuing or recent graduatesAdvanced degree (Master's or PhD) in data science, statistics, or related fields; more experience
Work EnvironmentEntry-level, training-focused roles in tech companies, startups, or research labsFull-fledged data analysis, modeling, and decision-making roles in various industries
Employer & Industry UsageCompanies hiring for entry-level machine learning roles, internships, or training programsOrganizations leveraging data science for strategic insights, product development, or research

The main difference between a Machine Learning Trainee and a Data Scientist lies in experience, responsibilities, and skill level. Trainees are typically beginners gaining foundational knowledge, while Data Scientists are experienced professionals performing complex data analysis and modeling tasks.

What are the key skills and qualifications needed to thrive as a Machine Learning Trainee, and why are they important?

To thrive as a Machine Learning Trainee, you need a solid understanding of mathematics, programming (especially Python), and foundational machine learning concepts, often supported by a relevant degree or coursework. Familiarity with tools like TensorFlow, scikit-learn, and data visualization libraries, as well as version control systems such as Git, is commonly expected. Strong problem-solving abilities, eagerness to learn, and effective communication help trainees excel in collaborative and fast-evolving environments. These skills and qualities are crucial for quickly adapting to new technologies, understanding complex data, and contributing meaningfully to machine learning projects.

What are Machine Learning Trainees?

Machine Learning Trainees are entry-level professionals or students who are learning the fundamentals of machine learning, including algorithms, data analysis, and model development. They often work under the guidance of experienced data scientists or engineers to gain hands-on experience with real-world datasets and tools. Their responsibilities may include data preprocessing, implementing basic models, and assisting in research or software development. This role is typically designed to help individuals build foundational skills needed for more advanced machine learning positions.
What are the most commonly searched types of Machine Learning jobs in Mississauga, ON? The most popular types of Machine Learning jobs in Mississauga, ON are:
What are popular job titles related to Machine Learning Trainee jobs in Mississauga, ON? For Machine Learning Trainee jobs in Mississauga, ON, the most frequently searched job titles are:
What job categories do people searching Machine Learning Trainee jobs in Mississauga, ON look for? The top searched job categories for Machine Learning Trainee jobs in Mississauga, ON are:
Machine Learning Trainee jobs near you
Infographic showing various Machine Learning Trainee job openings in Mississauga, ON as of July 2026, with employment types broken down into 1% As Needed, 79% Full Time, 18% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution.
Postdoctoral Researcher

Postdoctoral Researcher

University Health Network

Toronto, ON

Full-time

Posted 4 days ago

Job description

Company Description

UHN is Canada’s #1 hospital and the world’s #1 publicly funded hospital. With 10 sites and more than 44,000 TeamUHN members, UHN consists of Toronto General Hospital, Toronto Western Hospital, Princess Margaret Cancer Centre, Toronto Rehabilitation Institute, The Michener Institute of Education and West Park Healthcare Centre. As Canada's top research hospital, the scope of biomedical research and complexity of cases at UHN have made it a national and international source for discovery, education and patient care. UHN has the largest hospital-based research program in Canada, with major research in neurosciences, cardiology, transplantation, oncology, surgical innovation, infectious diseases, genomic medicine and rehabilitation medicine. UHN is a research hospital affiliated with the University of Toronto.

UHN’s vision is to build A Healthier World and it’s only because of the talented and dedicated people who work here that we are continually bringing that vision closer to reality.

www.uhn.ca

Job Description

Union: Non-Union
Number of vacancies: 1
New or Replacement Position: New 
Site: Krembil Research Institute
Department: Neuroimaging and Brain Modelling Laboratory
Reports to: Principal investigator
Hours: 37.5 hours per week
Salary Range: $54,902 - $93,333 per annum
Shifts: Monday to Friday
Status: Temporary Full-time  (1 year contract with possibility of extension)
Closing Date: August 13, 2026

Position Summary

Dr. Jürgen Germann’s Neuroimaging and Brain Modelling Laboratory at the Krembil Research Institute, University Health Network, is seeking a highly motivated Postdoctoral Research Fellow to lead a translational neuroimaging project focused on early diagnosis and disease progression modelling in Parkinson’s disease and related disorders.

The successful candidate will play a central role in developing MRI-based, machine learning-driven probability models for differential diagnosis using image-derived features. A key focus will be translating these models into a clinician-facing decision-support tool for real-world implementation. This includes building pipelines that take routine clinical MRI as input and generate patient-specific probabilistic diagnostic outputs to support clinical decision-making.

The position offers a unique opportunity to work at the interface of neuroimaging, machine learning, and clinical translation, with a strong focus on high-impact publications and the development of deployable clinical tools. The work will be conducted in a highly multidisciplinary environment, in close collaboration with Dr. Alexandre Boutet (Neuroradiology) and Dr. Anthony Lang (Neurology; Director of the Movement Disorders Program).

This opportunity will allow you to:

  • Lead development of machine learning models for imaging-based diagnostic probability estimation
  • Develop and optimize pipelines that generate patient-specific probabilistic predictions from MRI data
  • Design and implement a clinician-facing tool for model deployment and integration into clinical workflows
  • Analyze large-scale multimodal neuroimaging datasets (structural and diffusion MRI)
  • Perform statistical modelling, validation, and calibration of predictive models
  • Contribute to development of clinically interpretable outputs (e.g., probability maps, reports)
  • Work closely with clinical collaborators to ensure usability and relevance of the tool
  • Draft manuscripts, abstracts, and grant applications
  • Present research findings at meetings and conferences
  •  Mentor graduate and undergraduate trainees
  • Collaborate within a highly interdisciplinary team spanning neurology, neuroradiology, medical physics, and data science

Duties

  • Lead the development of machine learning models for imaging-based diagnostic probability estimation
  • Develop and optimize pipelines to generate patient-specific probabilistic predictions from MRI data
  • Design and implement a clinician-facing tool to support model deployment and integration into clinical workflows
  • Analyze large-scale multimodal neuroimaging datasets, including structural and diffusion MRI
  • Perform statistical modelling, validation, and calibration of predictive models
  • Contribute to the development of clinically interpretable outputs (e.g., structured reports, atrophy maps)
  • Collaborate closely with clinical partners to ensure usability, interpretability, and clinical relevance of developed tools
Qualifications
  • PhD in neuroscience, biomedical engineering, computer science, medical physics, or a related field (obtained within the past 5 years) required
  • Strong programming skills (e.g., Python, R, MATLAB, or similar)
  • Experience with machine learning and statistical modelling
  • Experience with neuroimaging analysis (MRI-based methods preferred)
  • Experience with voxel-based or morphometry-based neuroimaging analyses an asset
  • Experience developing end-to-end pipelines or tools for applied or clinical use an asset
  • Experience with model deployment (e.g., APIs, GUIs, or clinical software pipelines) an asset
  • Familiarity with neuroimaging toolkits (e.g., ANTs, FSL, FreeSurfer, SPM) an asset
  • Ability to work independently and lead projects
  • Strong analytical and problem-solving skills
  • Excellent written and verbal communication skills
  • Demonstrated scientific productivity (e.g., peer-reviewed publications)
  • Interest in translational and clinically impactful research

Additional Information

Why join UHN?
In addition to working alongside some of the most talented and inspiring healthcare professionals in the world, UHN offers a wide range of benefits, programs and perks. It is the comprehensiveness of these offerings that makes it a differentiating factor, allowing you to find value where it matters most to you, now and throughout your career at UHN.

  • Competitive offer packages
  • Government organization and a member of the Healthcare of Ontario Pension Plan (HOOPP https://hoopp.com/)
  • Close access to Transit and UHN shuttle service
  • A flexible work environment
  • Opportunities for development and promotions within a large organization
  • Additional perks (multiple corporate discounts including: travel, restaurants, parking, phone plans, auto insurance discounts, on-site gyms, etc.)

Current UHN employees must have successfully completed their probationary period, have a good employee record along with satisfactory attendance in accordance with UHN's attendance management program, to be eligible for consideration.

All applications must be submitted before the posting close date.
UHN uses email to communicate with selected candidates.  Please ensure you check your email regularly. At University Health Network (UHN), artificial intelligence technologies may be used to assist in the screening, assessment, and selection of candidates for this position.
Please be advised that a Criminal Record Check may be required of the successful candidate. Should it be determined that any information provided by a candidate be misleading, inaccurate or incorrect, UHN reserves the right to discontinue with the consideration of their application.
UHN is an equal opportunity employer committed to an inclusive recruitment process and workplace. Requests for accommodation can be made at any stage of the recruitment process. Applicants need to make their requirements known.
We thank all applicants for their interest, however, only those selected for further consideration will be contacted.