Utilize state-of-the-art model architectures, including transformers, for multi-modality tasks.
Curate and manage datasets from real-world scenarios to train models effectively.
Conduct training using large datasets and models on multi-GPU setups, demonstrating proficiency in data parallelism and model parallelism.
Develop efficient and well-documented code while actively participating in code reviews.
Deploy trained models on diverse platforms, such as robotic systems with ROS integration.
Design and implement user-friendly demo interfaces.
Optimize models for real-world applications, prioritizing accuracy, robustness, and speed.
Innovate and implement novel fusion algorithms to leverage complementary information from different modalities, with applications spanning transportation and healthcare domains.
Required
Master’s degree or higher in Computer Science, Mathematics, or related fields; PhD preferred.
Deep understanding of digital signal processing (e.g., Fourier transform), speech signal processing, image processing, and point-cloud processing.
Profound knowledge of Artificial Intelligence principles and techniques.
Proficiency in Python programming.
Hands-on experience with at least one of TensorFlow, Keras, or PyTorch.
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