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UCL Department of Chemical Engineering

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UCL Virtual Open Week - Chemical Engineering Talk and Q&A session

07 September 2020, 4:00 pm–5:45 pm

UCL Virtual Open Week - Chemical Engineering Q&A sessions

Interested in studying Chemical Engineering at UCL? Join our Talk and Q&A Session on Monday 7th September.

This event is free.

Event Information

Open to

All

Availability

Yes

Cost

Free

Organiser

Mark Bernardes – UCL Chemical Engineering

During the Q&A session you will have the unique opportunity to speak to admissions staff, academic tutors and also interact with current students to find out what studying at UCL is really like. Register today!

Please note, we will do our best to answer questions about the current Covid-19 situation, and what effects this may have going forward. Do visit UCL's main coronavirus webpage for the latest updates: https://www.ucl.ac.uk/coronavirus/faqs-schools-and-prospective-students

UCL uses a third party (Zoom) to administer our webinar/virtual open days and manage your personal information on our behalf. If you are happy for us to process your data solely for this purpose, please continue by entering your details below. Our Prospective Student Privacy notice is available here: https://www.ucl.ac.uk/legal-services/privacy/ucl-prospective-students-en...

About the Speaker

Dr Federico Galvanin

UG Programme Admissions Tutor at UCL Chemical Engineering

Federico Galvanin
Dr. Federico Galvanin is Lecturer and Undergraduate Admissions Tutor at UCL Chemical Engineering. He obtained his Master’s degree in Chemical Engineering in 2006 from the University of Padova (Italy) and the PhD degree in Industrial Engineering in 2010 from the same university. Dr. Galvanin’s teaching interests are in the areas of process modelling, design and simulation. He is the coordinator of the 4th year module “Process Systems Modelling and Design” and of the 3rd year module “Process Dynamics and Control”. His research interests lie at the interface between mathematical modelling and experimentation, with a specific expertise in the development and application of computational methods for fast model development using physics-based modelling, statistical planning and machine learning techniques. In his group algorithms are developed to optimally design experiments in automated platforms to identify predictive mathematical models with the minimum cost, time and effort. More about Dr Federico Galvanin