Description: This position offers the opportunity to conduct innovative research on a broad set of problems related to multi-modal temporal segmentation. Key Responsibilities: 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. Qualifications: 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.
Application Instructions: Please send an email to firstname.lastname@example.org with the following:
- Subject line including the job number you are applying for
- A cover letter explaining how your background matches the qualifications
- Candidates must have the legal right to work in the U.S.A.