Artificial Intelligence Internship (6 months) - Data Augmentation for few-shot learning
We are Schlumberger, the leading provider of technology and services to the energy industry. Throughout much of the oil and gas lifecycle in over 120 countries; we design, develop, and deliver technology and services that transforms how work is done.
We define the boundaries of the industry by unleashing our talented people’s energy. We’re looking for innovators to join our diverse community of colleagues and develop new solutions and push the limits of what’s possible. If you share our passion for discovery and want to find out what you could really do, then here is the place to do it.
Come and Join Schlumberger’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.
The Artificial Intelligence & Machine Learning Data Scientist helps develop software and processes that can be used for robotics, artificial intelligence programs and application. In close collaboration with the business and métiers, the data scientist offers mathematical and statistical models from the collected data to augment, improve or speed up human decisions within the Oil & Gas sector. With Schlumberger you will be given the opportunity to apply your expertise and deploy deep learning solutions at scale on real-world problems, supporting many areas of Schlumberger’s business. Schlumberger is the first Oil & Gas service company to move its processes and workflows in the cloud. This gives the Embedded AI Lab the perfect opportunities to leverage these innovative technologies and resources. As part of the Embedded AI Lab you will be able to test, experiment and research with the bleeding edge environment with Petabits and Petabits of data. You will be in charge of applying research and delivery of Proof of Concepts solutions, responding to a clear and specific business needs.
Over the last decade Deep Neural Networks have produced unprecedented performance on certain tasks such as classification and segmentation, given sufficient data. Effective training of these network usually requires a balanced, large dataset of input-output pairs. However, in some applications, the number of available annotated data is either insufficient or requires laborious, heavy human annotation efforts. This ‘few-shot’ learning problem - implying learning few training points for a large dataset is challenging.
Essential Responsibilities and Duties:
For this internship, we are interested in exploring a solution to few-shot segmentation learning problem by data augmentation. The idea is to generate more examples out of a few available. Classically, data augmentation consists in applying some transformations (rotations, shifts, brightness and color alterations …) to existing data. In the context of segmentation, standard data augmentation produces only limited plausible alternative data and does not generate the large dataset needed to boost the model performance. So, this project aims at developing a novel method using GANs (generative adversarial networks) to create large, realistic-looking, synthetic datasets to resolve a segmentation task.
Schlumberger 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.