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Department of Computer Science and Technology

We are offering PhDs and postdocs in science disciplines across Cambridge a free 'Data for Science' training course this summer. Aimed at helping participants to apply data analysis to their own datasets and problems, the course runs over five weeks, starting on 20th July. 

This course is part of the new Accelerate Programme for Scientific Discovery, which is being funded by a generous donation to the University from Schmidt Futures. The programme - led by Professor Neil Lawrence, DeepMind Professor of Machine Learning here - is designed to equip researchers in science disciplines outside computer science with the skills they need to use machine learning in their research.

"Machine learning and AI are increasingly part of our day-to-day lives, but they aren’t being used as effectively as they could be, due in part to major gaps of understanding between different research disciplines," says Neil. "This programme will help us to close these gaps by training physicists, biologists, chemists and other scientists in the latest machine learning techniques, giving them the skills they need while accelerating the excellent research already taking place here at the University."

Data for Science Residency Programme 20th July - 21st August
The 'Data for Science Residency Programme' starts on 20th July. The course will be run by Cambridge Spark and will provide scientists with modern practical data analysis skills using Python in a virtual instructor-led accelerated masterclass.

As well as learning how to apply data analysis to their own datasets and problems, virtual residents on the course will collaborate on real-world data science challenges in teams as part of two hackathons and be part of a world-leading community of scientists and researchers.

Participants will be current PhD students or researchers at the University of Cambridge in science disciplines with basic programming skills. Applicants were asked to commit to dedicating at least 30 hours per week to the course over the five weeks from Monday 20th July - Friday 21st August 2020 and made aware that, as places are limited, participants would be selected based on a technical assessment and alignment with the aims of the programme.

Applications closed on Friday, 3rd July. 

Published by Rachel Gardner on Tuesday 30th June 2020