Machine Learning & Optimization R&D Intern

About Panevo

At Panevo we combine our passion for sustainability and engineering to enable industry to optimize utility and process performance, reduce costs and ensure environmental compliance. From strategy, to site auditing and project implementation, we deliver long-term benefit to local businesses and global brands.

We have two main software products:

- ioTORQ EMIS: an energy management platform for industrial facilities, with built-in machine learning capabilities

- ioTORQ LEAN: a lean manufacturing application to detect production downtimes in real time and help improving industrial performance.

About this opportunity

We are looking for a Machine Learning & Optimization intern to join our R&D team in our downtown Vancouver office. You will work closely with our Lead Machine Learning Developer on applied research projects that use industrial data to reduce the energy consumption, costs and emissions of manufacturing processes.

A central project for this internship is the optimization of a large natural gas fired veneer dryer at a wood products mill in British Columbia. Using sensor and production data collected through ioTORQ EMIS (temperatures, moisture readings, gas consumption, throughput), the goal is to build a data-driven model of the dryer and develop control strategies that reduce gas consumption while maintaining product quality. The resulting recommendations are reviewed with plant engineers and assessed against measured energy savings.

Depending on your profile, you will work on topics such as:

• Data-driven modelling of industrial equipment (surrogate models, system identification, digital twins)
• Reinforcement learning, in particular offline and model-based RL trained on historical data and simulators
• Optimization methods such as Bayesian optimization, model predictive control and metaheuristics
• Time-series forecasting and anomaly detection on energy data
• Applications of generative AI to energy and process data analysis

Key duties

• Explore, clean and analyze industrial time-series data, and assess its quality and limitations
• Build and validate predictive models of energy use and product quality
• Design, implement and benchmark optimization and reinforcement learning approaches against these models
• Translate results into clear, actionable recommendations for engineers and plant operators
• Document methods and results, and present findings to the team
• Work with our software developers to bring prototypes into the ioTORQ EMIS platform
• Follow relevant research and propose new approaches

Internship details

• Duration of 6 to 12 months with a flexible start date. Final-year internships (stage de fin d’études) and gap-year internships (césure) are both welcome
• On site at our downtown Vancouver office
• The internship must be a required part of your degree, with an internship agreement (convention de stage) signed by your school
• Applicants must hold citizenship of a country eligible for the International Experience Canada (IEC) International Co-op category, such as France (see list below)

Essential skills & attributes

• Currently enrolled in an engineering school or a Master’s program in engineering, applied mathematics or data science
• Proficient in Python
• Experience with a machine learning framework such as scikit-learn
• Fundamentals in energy sciences: thermodynamics, fluid dynamics and heat transfer
• Fundamentals in machine learning
• Fundamentals in control and process engineering
• Professional English
• Able to explain technical results clearly to non-specialists
• Self-driven and able to work autonomously
• Strong interest in energy efficiency, industrial processes and decarbonization

Preferred experience and knowledge base

• Hands-on experience with reinforcement learning (coursework or personal projects)
• Professional software development practices, including version control with Git

Eligible countries of citizenship

Countries with access to the IEC International Co-op (Internship) category: Australia, Austria, Chile, Costa Rica, Croatia, Czech Republic, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Latvia, Lithuania, Luxembourg, Norway, Poland, Portugal, Republic of Korea, Slovakia, Slovenia, Spain, Sweden, Switzerland, Taiwan and the United Kingdom.

Eligibility conditions may change; please check your country’s requirements before applying.