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Friday, November 8, 2019 1:00 pm - 1:00 pm EST (GMT -05:00)

PhD Defence | Cellulosic Nanocomposites for Advanced Manufacturing, by Andrew Finkle

You are welcome to attend Andrew Finkle's PhD defence, in which he will discuss his research into the formulation and processing of thermoplastics and composites containing Nanocrystalline Cellulose (NCC) for advanced manufacturing techniques.

Saturday, November 9, 2019 10:00 am - 7:00 pm EST (GMT -05:00)

What's Next/What Now conference

This full day conference for first and upper year engineer students will showcase a variety of guest speakers and workshops focused on helping participants navigate through both student life and career preparation. From planning out your next four years in undergrad to learning what to expect when you enter the workforce, this conference will help you gain the skills and knowledge needed to achieve your goals with confidence.

The Department of Cheical Engineering welcomes you to a seminar by Dr. Youri Gendal from Technion - Israel Institute of Tehnology, who will be speaking about a catalytic hydrogenation treatment process that may provide an economical and practical solution to groundwater, and specifically nitrate, pollution.

Wednesday, November 20, 2019 11:30 am - 1:00 pm EST (GMT -05:00)

Vote for your CEGSA Reps on Wednesday Morning

Make your opinion count!

The Chemical Engineering Graduate Student Association (CEGSA) will be holding its annual elections on Wednesday, November 19, in E6-2024.

Vote for what you want to see happen in the Chemical Engineering community next year. And enjoy free pizza. 

CEGSA organises cultural activities and social events to provide a more enriched research experience for graduate students in the department. It also represents chemical engineering graduate students in departmental and university-wide committees.

You are invited to attend Yael Izamal Valdez Navarro's MASc oral exam, where they will present a decomposition algorithm for obtaining robust scheduling and control decisions. It iteratively solves scheduling and dynamic optimization problems while approximating stochastic uncertainty through back-off terms, calculated through dynamic simulations of the process.