Internship - Ai Engineer - Model Adaptation for - Clamart, France - Schlumberger

Schlumberger

Entreprise vérifiée

Clamart, France

il y a 2 semaines

Posté par:

Sophie Dupont

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Description

Job title:

Internship - AI Engineer - Model adaptation for Borehole images (6 months)

About Us:

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.

Location:
Clamart, France

Come and Join SLB's AI Lab in Paris. We are currently offering internship to bright minds specialized in Data Science and Artificial Intelligence. Discover a multinational company. We have brought a little bit of the Silicon Valley in Paris.

Experience working within a team of young and fun passionate Data Scientists, tackling real business challenges, in tandem with business experts who are sitting at your desk.

Description:

Borehole image data acquired in subsurface environments (underground) differs significantly from any conventional image datasets used in model design among computer vision researchers across the world.

This is mainly due to a variety of sensing technologies available in the tools employed for data acquisition in Energy industry.

Such differences result in an image that spans more than 100,000 rows with only a few dozen columns.

They depict various periodic patterns in x and y directions and are a result of specific mapping from 3D to 2D space.

Because the images are so long, they cannot fit into the memory during training, and we are usually splitting them into small patches.

Moreover, only a very small fraction of the input images is informative of the label of interest, resulting in a low region of interest (ROI) to image ratio.

However, most of the popular convolutional neural networks (CNNs) are designed for images that have relatively large ROIs and have a balanced height-to-width ratio.

Required

skills:

Skills: applied mathematics, probability & statistics, image processing, deep learning models like CNNs, Siamese Networks, Autoencoders
Programming language: Python, PyTorch or Keras/Tensorflow

References:

Yu, Fisher, and Vladlen Koltun. "Multiscale context aggregation by dilated convolutions.

" arXiv preprint arXiv:

Dai, Jifeng, et al. "Deformable convolutional networks." Proceedings of the IEEE international conference on computer vision. 201
Cai, Zhaowei, et al. "A unified multiscale deep convolutional neural network for fast object detection." European conference on computer vision. Springer, Cham, 2016.

SLB is an equal employment opportunity employer.

Qualified applicants are considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or other characteristics protected by law.