Atos IT Challenge 2019

Discover the 15 shortlisted teams!

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The Atos IT Challenge is an annual competition for engineering students from all around the world

This competition encourages the next generation of IT professionals to achieve their personal best just like our teams within the Atos Group are doing. This competition represents a great opportunity for Atos to show just how much talent exists within the next generation as they will shape the companies of the future.

As a forward-looking IT company, we always strive to invest in the future.

The contest is open to students, from any grade. Each University can present as many teams as they like, however but each team needs to be composed of a minimum of two and a maximum of four members.

After the registration phase and a first Jury, a Coach from the Atos Scientific Community is assigned to each shortlisted team to help them take the idea through to proof of concept. The Coach provides help with technical and business insights, as well as advice to improve their all-important presentation skills necessary for business in the technology world.

A Jury, consisting of Atos Executives and external partners selects three finalists at the end of a Jury Day. During this day all shortlisted teams, present their solution including a business model in front of the Jury.

The challenge is split into 5 main phases

Find more about the Atos IT Challenge

Separation dash

The Challenge for the 2019 edition: Machine Learning for Sustainability

Machine Learning is a field of Artificial Intelligence that uses statistical techniques to give computers the ability to “learn”. It is a game changing technology, already being applied in many areas including Video & Serious Gaming, Medical Diagnosis, Fraud Detection, Driverless Vehicles and Security Surveillance. For all its benefits, some are also saying it could be a huge threat – with calls for regulation to protect humanity against AI running out of control. We believe it is an important area for the Atos IT Challenge to explore.

We are seeking how Machine Learning can be applied to the topic Sustainability, in which the exploitation of resources, the direction of investments and the orientation of technological development are all in harmony.

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