Internship opportunities at Prophesee
PROPHESEE
Founded by the world’s leading pioneers in the field of neuromorphic vision, Prophesee develops computer vision sensors and systems for application in all fields of artificial vision. The sensor technology is inspired by biological eyes, acquiring visual information in an extremely performing yet highly efficient way. Prophesee’s disruptive vision sensor technology entirely overthrows the established paradigms of frame-based vision acquisition currently used everywhere in computer vision.
This is a great opportunity to join a dynamic company and an exciting team and to lead a paradigm shift in computer vision across many industries.
EVENT-BASED TECHNOLOGY
Prophesee designs and produces a new type of cameras that are bio-inspired and thus free themselves from the concept of images. They do not gather information with a fixed framerate but instead each pixel is captured asynchronously when needed. These bio-inspired cameras, also called event-based cameras or neuromorphic cameras, therefore have an extremely sparse output and enable, with appropriate algorithms, a real time treatment of the information at an equivalent frequency of a kHz or more. But since the data coming from the sensor are quite different from the images traditionally used in standard vision, Prophesee is also advancing the algorithmic and machine learning side of this new kind of machine vision. This enables our clients to build new applications in a wide range of domains, including industrial automation, connected devices, autonomous vehicles, augmented and virtual reality, and more.
INTERNSHIP POSITION DESCRIPTION
We are looking for passionate interns who demonstrate initiative, take ownership for project work, and exhibit a strong spirit of innovation. The ideal candidate is a curious and creative individual keen on problem-solving and with prior experience in C++ / Python programming and exposure to the Computer Vision / Image Processing / Artificial Intelligence / Machine Learning domains.
She/He will work in a mixed team of scientists and engineers to design, develop & optimize solutions to research problems. Her/His main contribution will be in creating innovative bio-inspired computer vision algorithms for specific tasks across many applications such as computational imaging, 3d sensing, robotics, localization, factory automation, smart devices, aerospace & defense, automotive, etc.
The main required skills common to MOST internship positions are the following (but not exclusively):
- Excellent programming skills in C++ and/or in Python
- Engineering background in Computer Science, Mathematics or related field
- Prior experience in projects involving at least one of the following domains:
- Algorithmic design, e.g. 3d vision, machine learning, numerical optimization, etc
- Software development, e.g. implementation, architecture, optimization, testing, porting on embedded platforms, etc
- Development operations, e.g. source code versioning, continuous integration & deployment, cloud computing, system administration, etc
R&D
Event-based Transformers
Visual Transformers (ViT) have become the predominant deep learning architecture for many computer vision tasks (object detection, flow estimation, tracking...). Following this trend of research, recently ViT architectures have been adapted and applied to event-based data. However, it is not clear what is the best way to apply ViT to event data or how to train it.
The goal of this internship is to explore self-supervised training strategies for ViT on fundamental event-vision tasks.
During the project you will work with the R&D team on the following steps:
- Review of the state-of-the-art for ViT
- Development of a ViT architecture for event-based data
- Validate the proposed solution on real world datasets and compare with PSEE internal solutions.
BENEFITS
Salary package : 80% SMIC (9,32 €/h)
Meal vouchers : 9€ / working day (Swile card)
Working hours : 35h / week
Office location : Paris Bastille (74 rue du Faubourg Saint Antoine 75012)
Other benefits : CSE