Graduate Research Intern

About Basetwo

We're on a mission to make manufacturing faster, smarter, and more resilient.

Basetwo is a fast-growing Toronto-based startup backed by some of the top VCs in the Boston, New York, and Bay Area ecosystems. We work with some of the largest Fortune 500 chemical and pharmaceutical manufacturers and provide their process engineers with a next generation AI platform. Our platform provides these engineers with the ability to build digital twins and receive real-time recommendations on the next best action they can take in their plants to improve efficiency as well as reduce costs and cycle time.

The Role

The Graduate Researcher at Basetwo will have the opportunity to be on the forefront of modelling, optimization and control of pharmaceutical and chemical manufacturing processes. They will play a key role in delivering industry value to our Fortune 500 manufacturing customers by researching, developing and validating process models for control and optimization applications. They will have the opportunity to work closely with interdisciplinary teams of process and software engineers, product designers and domain experts for continuous career growth and learning.

What You'll Do

• Research, develop and validate dynamic process models of batch and continuous systems for control and optimization applications
• Deliver robust, well-tested code that adhere to community standards and best practices
• Summarize research activities and model structure in well-structured product specification docs
• Support software engineering teams productionize your work to the Basetwo platform
• Test and QA/QC delivered models on Basetwo platform
• Support ongoing optimization and control algorithm research projects/efforts, as needed
• Attend meetings and deliver presentations to communicate research findings, progress and deliverables to relevant partners across multiple disciplines
  
  
  

Requirements

What You'll Need

• Candidate for Masters or PhD in Chemical Engineering or a related technical field
• Ability to develop research-grade code in Python leveraging scientific computing libraries
• 2-4 years of academic and industry experience in chemical or pharmaceutical simulation, control and optimization
• Demonstrated track record of starting and leading interdisciplinary research and engineering projects
• Experience communicating projects to both technical and non-technical audiences
• Experience working on cross-functional teams