If you are interested in applying machine learning to real-world industrial challenges, specifically in predictive maintenance using time-series sensor data - this is a perfect opportunity for you. Be a part of a team of research and machine learning scientists developing models to predict equipment failures and optimize maintenance strategies, and get mentored by some of the best minds in AI while doing it.

- Dave Staszak, Lead Machine Learning Scientist

About the Role

This is a paid Residency that will be undertaken over an eight-month period with the potential to be hired by our client afterwards (note: at the discretion of the client). The Resident will be reporting to an Amii Scientist and regularly consult with the Client team to share insights and engage in knowledge transfer activities.

About our Client

Supreme International is recognized as one of the best feed mixer manufacturers in the world. Supreme entered the global market very early on as a manufacturer working with markets in Saudi Arabia, Japan and Australia and now has a broader presence in Asia, Africa, the Middle East, and the Americas. Supreme continues to be the benchmark for quality equipment globally. Often paired with the words performance and reliability, Supreme feed mixers have found a place on the world stage. Supreme's main facility is located in Wetaskiwin, Alberta. In 2010, Supreme expanded into the US with a truck mount assembly facility and a parts distribution warehouse located in Dodge City, Kansas. This centralized location improved parts distribution throughout North America and is a more efficient location for truck mount assembly.

About the Project

The project will leverage Supreme Internationals current control and monitoring system to develop a predictive maintenance system for Supreme Internationals GEN3-controlled animal feed mixers by leveraging machine learning models. The project will analyze historical and ongoing data generated from Supremes GEN3 control and sensor systems to determine machine learning feasibility in predictive maintenance. The project will involve collecting and analyzing operational and failure data from Supreme Internationals GEN3 system to develop one or more ML models for predictive maintenance.

Who Are You

You have completed a graduate-level program or higher (M.Sc/Ph.D) in Computing Science, ML, or Engineering, with substantial research or project experience in machine learning, deep learning, and/or time series analysis. You are proficient in Python and familiar with key ML frameworks and libraries such as Scikit-learn, TensorFlow, PyTorch, and Pandas. Your positive attitude towards learning new applied domains and your ability to communicate technical concepts clearly make you a valuable team player. You are enthusiastic about collaborating across interdisciplinary teams to achieve excellence.

What You Will Be Doing

In this role, you will be instrumental in developing machine learning models for predictive maintenance of Supreme International's feed processing equipment. Your work will focus on analyzing time-series sensor data to detect anomalies and predict potential failures. You will explore and implement both supervised and unsupervised learning techniques, and various classification models. You will be responsible for data preprocessing, including handling missing data, noise reduction, and synchronization issues. You will also contribute to the development of strategies for failure labeling and defining appropriate time windows for model training and prediction. You will collaborate with interdisciplinary teams, participate in project meetings, and contribute to reports on model performance and project milestones. Your efforts will directly contribute to shifting Supreme International from a reactive to a proactive maintenance strategy, reducing downtime, optimizing maintenance schedules, and enhancing overall operational efficiency.

Required Skills / Expertise

Were looking for a talented and enthusiastic individual with solid knowledge of machine learning, demonstrated experience with supervised learning, and experience in applied settings.

Key Responsibilities:

• Design, implement, optimize, and evaluate time series machine learning models tailored for predictive maintenance and anomaly detection, with a specific focus on developing, training, and refining solutions for analyzing sensor data from feed processing equipment.
• Prepare and curate high-quality, time series datasets for model training and validation from diverse sources.
• Utilize state-of-the-art machine learning frameworks and tools, including TensorFlow, PyTorch, Scikit-learn, and Pandas, to enhance model performance and streamline data processing.
• Collaborate with cross-functional teams to build and deploy predictive maintenance solutions that address client needs, ensuring seamless integration into existing systems.
• Engage in regular client meetings, contributing insights and updates on model performance and project milestones through presentations and detailed reports.
• Optimize machine learning pipelines to ensure efficient and scalable time series analysis and anomaly detection capabilities, leveraging techniques like ARIMA, LSTM, and matrix profiling.

Required Qualifications:

• Completion of a graduate level program or higher (M.Sc/Ph.D) in Computing Science, ML or Engineering
• Research or project experience in time series analysis, anomaly detection, and predictive maintenance use cases
• Proficient in Python programming language and related ML frameworks, libraries, and toolkits (e.g., Scikit-learn, TensorFlow, PyTorch, Pandas)
• A positive attitude towards learning and understanding a new applied domain
• Must be legally eligible to work in Canada

Preferred Qualifications:

• Previous experience applying machine learning to time series data for predictive maintenance problems
• Experience with ARIMA, LSTM, matrix profiling, and autoencoders
• Familiarity with sensor data characteristics and challenges
• Experience with deploying machine learning models in production environments or strong software engineering (or MLE) skills is a plus

Non-Technical Requirements:

• Desire to take ownership of a problem and demonstrated leadership skills
• Interdisciplinary team player enthusiastic about working together to achieve excellence
• Capable of critical and independent thought
• Able to communicate technical concepts clearly and advise on the application of machine intelligence
• Intellectual curiosity and the desire to learn new things, techniques, and technologies

Why You Should Apply

Besides gaining industry experience, additional perks include:

• Work under the mentorship of an Amii Fellow and Amii Scientist for the duration of the project
• Participate in professional development activities
• Gain access to the Amii community and events
• Build your professional network
• The opportunity for an ongoing machine learning role at the clients organization at the end of the term (at the clients discretion)

About Amii

One of Canadas three main institutes for artificial intelligence (AI) and machine learning, our world-renowned researchers drive fundamental and applied research at the University of Alberta (and other academic institutions), training some of the worlds top scientific talent. Our cross-functional teams work collaboratively with Alberta-based businesses and organizations to build AI capacity and translate scientific advancement into industry adoption and economic impact.

How to Apply

If this sounds like the opportunity you've been waiting for, please dont wait for the closing April 18, 2025 to apply - were excited to add a new member to the Amii team for this role, and the posting may come down sooner than the closing date if we find the right candidate before the posting closes! When sending your application, please send your resume and cover letter indicating why you think you'd be a fit for Amii. In your cover letter, please include one professional accomplishment you are most proud of and why.

Applicants must be legally eligible to work in Canada at the time of application.

Amii is an equal opportunity employer and values a diverse workforce. We encourage applications from all qualified individuals without regard to ethnicity, religion, gender identity, sexual orientation, age or disability. Accommodations for disability-related needs throughout the recruitment and selection process are available upon request. Any information provided by you for accommodations will be kept confidential and wont be used in the selection process.