Vacancy expired!
- Propose, create, and implement supervised and unsupervised data understanding algorithms from multimodal and multisensory data streams obtained from human and environmental monitoring sensors.
- Develop and evaluate metrics to verify reliability of the proposed algorithms.
- Participate in ideation, creation, and evaluation of related technologies in various mobility-oriented domains.
- Contribute to a portfolio of patents, academic publications, and prototypes to demonstrate research value.
- Participate in data collection, sensor calibration, and data processing. Compare learned features vs. engineered features for time series data. Implement state-of-the-art classification and regression models.
- Research experience in multimodal sensory signal processing (e.g., vision, speech, vehicle sensory data, human behavioral data & physiology), and machine learning.
- Strong familiarity with machine learning techniques pertaining to sequential data processing.
- Experience in open-source Deep Learning frameworks such as TensorFlow and PyTorch.
- Highly proficient in software engineering using Python.
- Strong written and oral communication skills including development and delivery of presentations, proposals, and technical documents.
- Representative publications in one or more of the following areas: signal processing and machine learning.
- Ph.D. or similar level knowledge in computer science, electrical engineering, or related field.
Vacancy expired!