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The perception and machine learning team is tasked to apply machine learning to the automotive industry. Applications include autonomous driving, manufacturing, material design, etc. The team develops state of the art AI solutions to solve complex and challenging problems by leveraging the latest techniques in machine learning on large data sets. At ICC, you will be involved in developing modern methods in the field of sensor data processing to enable safe and robust automated driving in any scenario. During this fellowship, you will be supporting a team of AI researchers and engineers to find suitable learning methods for robust perception.
Job Responsibility:
Driving data pre-processing including checking labels, formatting, etc.
Support project team in building data pipelines for training deep neural networks
Benchmarking and reporting of various neural network models performance
Research into suitable new network architectures to for time series prediction, and anomalies detection
Research into suitable new network architectures to improve the perception of the environment with a focus on optical sensors
Research on supervised and unsupervised learning methods to estimate road participants' depth, motion, and velocity
Research on various neural networks for the interpretation of camera images (object detection, panoptic segmentation) and trajectory/motion planning
Fusion of different neural networks to use shared resources to decrease memory and computation footprint
Requirements:
Must be enrolled at a University/College or Graduation date must be within the last six months
Must have a cumulative GPA of at least 3.0
Strong Python programming skills
Good experience using Linux
Experience with deep learning frameworks such as TensorFlow and PyTorch
Good knowledge of image processing and machine learning
Independent work, initiative, motivation, ability to work in a team
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