Thèse CIFRE

Scortex is hiring!

About

Scortex deploys artificial intelligence in the heart of factories. We help our customers take the next big leap in smart automation thanks to our Quality Intelligence Solution.

Our platform enables manufacturing companies to take control over their quality:

  1. Automate complex visual inspection tasks at any stage of the manufacturing process
  2. Monitor key quality data through innovative dashboards
  3. Improve the production process by consolidating production knowledge

Scortex is the next evolution of the quality control.

Find below some press articles related to Scortex (French content):

AI for visual inspection, Techniques de l'ingénieur
Scortex is a founding member of the AI Factory with Microsoft & INRIA @ Station F
Interview of Scortex's CEO: Aymeric de Pontbriand

What you will do

The goal of Scortex is to be able to deploy quickly its solution for any new client or reference. Since Scortex technology currently relies mostly on supervised algorithms, an extensive manual annotation process is required. State of the art shows that it is possible to alleviate the need for annotations through several techniques. One of these is the use of unsupervised / semi supervised and weakly supervised learning.

The goal of the thesis is thus to design and implement a fast, non purely supervised defect detection system on high resolution images and videos. The thesis will typically include the study of deep anomaly detection systems. The candidate may have to explore several leads from the bibliography such as auto encoders, GANS, metric learning and siamese networks or more traditional methods for anomaly detection (LOF, One class SVM)...

Scortex is looking for a way to proof its supervised algorithms and to be able to generate smart candidates for labelling. As a result, the anomaly detection system should ideally give a segmentation mask of the defect, frame by frame.

As a proactive member of the machine learning and computer vision team, your work will include a varied range of challenges:

  • explore various state of the art techniques to help solve tasks currently unbeaten by computers;
  • stay on the bleeding edge of research and participate actively in the community;
  • design, develop and implement supervised and unsupervised models with extremely constraining requirements not only on accuracy, but also on real-time execution, fast and scalable training processes and minimal annotation levels;
  • help improve our pipelines of data acquisition, training and inference.

What we are looking for

  • In-depth knowledge of deep learning techniques applied to computer vision: deep convolutional networks, autoencoders, image (pre)processing, regularization;
  • Proficient knowledge of both supervised and unsupervised machine learning techniques : clustering, object detection, generative models, dimensionality reduction;
  • Understanding of standard computer vision techniques : filtering, transformations, descriptors and detectors;
  • Knowledge and understanding of the mathematics underlying all of the above : probability and statistics, optimization, linear algebra, numerical computation;
  • Proven experience with at least one machine learning framework (bonus points for Keras or Tensorflow);
  • Good programming and software engineering skills;
  • Experience with the unix environment.

Additional Information

  • Contract type: Full-Time
  • Location: Paris, France (75013)
  • Education Level: Master's Degree