M2 internship position: AI for telecommunications in adverse environments (Schlumberger)
24 Novembre 2021
Catégorie : Stagiaire
The goal of this internship is to develop state-of-the art artificial intelligence algorithms (deep learning, reinforcement learning, etc.) to optimize telecommunication systems in adverse environments (mud-pulse telemetry, underwater acoustic communications, telemetry through pipes, etc.). Possibility of a Ph.D. sponsorship in continuation of the internship.
Our story begins with what it truly means to be a technology innovator. It stems from a common sense of purpose that unites the people of Schlumberger who, representing more than 160 nationalities, provide leading digital solutions and deploy ground-breaking technologies to enable performance and sustainability that are crucial for the global energy industry. With expertise in more than 120 countries, we partner with customers in close collaboration to create industry-changing technologies that unlock cleaner, safer access to energy for every community— including those we live and work in.
Schlumberger Riboud Product Center (SRPC)
is the largest Schlumberger technology and development center in Europe. Around 800 scientists, engineers, and technicians, of more than 50 nationalities, design and manufacture equipment and systems for our oilfield services worldwide. Based in Clamart close to Paris, SRPC teams form a center of excellence for research and development of breakthrough technologies. SRPC is recognized globally for its expertise in:
Mechanical systems for extreme conditions
Physics of sensors and measurements
Geophysics and geology
Our strength comes from our passion for innovation and our multicultural population.
SCOPE AND SUBJECT OF THE INTERNSHIP
Telecommunication in Schlumberger is ubiquitous and is a key technology differentiation in its service portfolio: Mud-pulse telemetry provides real time geophysical data while drilling, Underwater acoustic communication enables autonomy of subsea robots, and Acoustic telemetry trough pipes allows two-ways real-time downhole testing.
The telecommunication systems we are developing are typically characterized by a strongly adverse environment with frequency selective propagation channels, strong noise variability and changing conditions over time. Because of the lack of accurate analytical models for the signal generation, propagation and noise, the goal of this internship is to develop state-of-the art artificial intelligence algorithms (deep learning, reinforcement learning, …) to learn such models. These algorithms will be evaluated in connection with the improvement of the physical layer: Clock mismatch tracking, Equalization, Noise cancellation, Decoding and decision, Error correcting codes, …
Possibility of a Ph.D. sponsorship in continuation of the internship.
DATE AND LOCATION
Starting date: Feb/Mar 2022
Duration: 4-6+ months
Location: Schlumberger, Clamart, France
Master’s Degree - Final year of Engineering school with a solid background in either: applied mathematics, signal processing, telecommunications or machine learning
Working knowledge of relevant software, C++/Python/Matlab. Experience in software defined radio would be a plus (GNU-radio, Seeedstudio, NESDR…).
Working knowledge of relevant machine learning frameworks (Tensorflow, Pytorch, Theano…)
RESPONSIBILITIES & DELIVERABLES
Propose a technical solution addressing the technical needs
Implement a proof of concept of the solution, evaluate the performances
Design/Conduct experiments with simulated data or real data
Produce tested, commented, and archived quality code (git/Devops)
Present results to stakeholders and relevant technical communities (Internal & External)
Interact with research scientists from our research centers (Boston/US, Cambridge/UK)
Develop technical understanding and get ownership on the design of a commercial solution
Applications should include a CV, a statement of interests and motivations, as well as academic transcripts (Master level) and must be sent to Arnaud Jarrot (email@example.com) and François-Xavier Socheleau (firstname.lastname@example.org).