Vacancy caducado!
- Research and development of video-based computer vision algorithms that enable interpretation and understanding of complex traffic scenes involving a high degree of interaction between road users and the environment for various driving assistance technologies
- Research and development of supervised and unsupervised mechanisms (e.g. attention) that identify “important agents” that potentially influence the ego-vehicle’s future trajectory and the driver’s decision-making process
- Design, development, and integration of software systems and architectures necessary to realize research prototypes
- Develop and evaluate metrics to verify the reliability of proposed algorithms
- Contribute to a portfolio of patents, academic publications, and prototypes to demonstrate research value
- Ph.D. or M.S. in computer science, electrical engineering, or related field
- Strong familiarity with machine learning techniques pertaining to visual scene understanding
- Familiarity with scene modeling and interpretation using spatiotemporal graphs, scene graphs, graph convolution networks, or similar graphical modeling techniques
- Experience in open-source Deep Learning frameworks such as TensorFlow or Pytorch preferred
- Highly proficient in software engineering using C and Python
- Hands-on experience in handling multi-modal sensor data preferred
- Strong written and oral communication skills including development and delivery of presentations, proposals, and technical documents
- Strong publication record in computer vision or machine learning
Vacancy caducado!