This position offers the opportunity to conduct innovative research on a broad set of problems related to multi-modal temporal segmentation.
- Propose, create, and implement supervised and unsupervised data segmentation/clustering algorithms from multimodal and multisensory data streams obtained from traffic scenes.
- Develop and evaluate metrics to verify reliability of the proposed algorithms.
- Participate in ideation, creation, and evaluation of related technologies in various domains other than traffic scenes, including temporal segmentation of human activities.
- Contribute to a portfolio of patents, academic publications, and prototypes to demonstrate research value.
- Participate in data collection, sensor calibration, and data processing.
- Participate in software development and implementation on various experimental platforms.
Must Have Skillsets:
- PhD in computer science, electrical engineering, or related field.
- Research experience in computer vision, machine learning, and multi-modal signal processing.
- Strong familiarity with machine learning techniques pertaining to sequential data processing.
- Preferred hands on experience in handling multi-modal sensor data.
- Preferred experience in open-source Deep Learning frameworks such as TensorFlow or Caffe.
- Highly proficient in software engineering using C++ and Python.
- Strong written and oral communication skills including development and delivery of presentations, proposals, and technical documents.
- Strong publication record in one or more of the following areas: computer vision, machine learning, or computer vision.
Location: Mountain View, CA
Type: Direct-hire or Contract