Internship - High Performance Computing (6 months)

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

We are a global technology company, driving energy innovation for a balanced planet.

At SLB we create amazing technology that unlocks access to energy for the benefit of all. That is our purpose. As innovators, that’s been our mission for 100 years. We are facing the world’s greatest balancing act- how to simultaneously reduce emissions and meet the world’s growing energy demands. We’re working on that answer. Every day, a step closer.

Our collective future depends on decarbonizing the fossil fuel industry, while innovating a new energy landscape. It’s what drives us. Ensuring progress for people and the planet, on the journey to net zero and beyond. For a balanced planet.

Our Purpose

Together, we create amazing technology that unlocks access to energy for the benefit of all.
You can find out more about us on https://www.slb.com/who-we-are

Job title: Internship - High Performance Computing (6 months)

Location: Clamart, France

Scope of the internship:

Communication systems play a growing role in the field of robotics, where remote systems must be interconnected. Schlumberger has developed communication systems for extreme environments such as subsea and underground. Those communication systems are based on heavy real-time computations that execute the tasks related to signal processing, data compression, AI and coding. The intern will implement advanced algorithms in Python or C++ to evaluate the performance of new techniques.

Responsibilities:

The intern will be responsible of the implementation of signal processing algorithms in C++. This work will build on existing algorithms that were first implemented in Matlab or Python for fast prototyping. The intern will optimize the implementation to reach high performance computing and evaluate the performance with data-driven approaches.