About the Team The team at PICO is dedicated to leverage technologies such as computer vision, deep learning, SLAM, 3D reconstruction, and multi-sensor fusion, we continuously expand the ways humans interact with the virtual world through handheld controllers, bare-hand tracking, eye-tracking, and XR interactive accessories, enhancing the overall interaction experience.
Responsibilities：
- Research and develop innovative deep learning models with a focus on the intersection of surface electromyography (sEMG), computer vision, and IMU technologies；
- Design and implement sEMG signal acquisition pipelines, optimize signal quality, and perform data preprocessing tasks such as denoising and feature extraction；
- Explore spatiotemporal feature fusion methods (e.g., Transformer, LSTM, Spatiotemporal Convolutional Networks) to achieve efficient multimodal data fusion；
- Handle sensor noise and interference in various environments, optimize model performance, and improve the generalization of algorithms
Requirements
Minimum Qualifications:
- Master’s degree or above in machine learning, signal processing, computer science, statistics, speech and language technology, or a related field；
- Proficient in the fundamentals of deep learning, with substantial experience in training and deploying deep models. Experience in fine-tuning end-to-end speech recognition frameworks (e.g., Conformer, RNN-T, LAS, CTC) and familiarity with 2D/3D perception algorithms in computer vision.；
- Familiarity with sEMG signal acquisition and processing, including signal denoising (e.g., filtering algorithms) and feature extraction (e.g., time-domain and frequency-domain features)；
- Experience with common digital signal processing techniques, such as digital filtering, Fourier transforms, correlation analysis, and modulation.
Preferred Qualifications:
- Experience in analyzing and modeling high-dimensional time-series data, such as speech signals, neural signals, physiological signals, videos, or other sensor data.
- Publications in accredited academic conferences (e.g., CVPR, ICCV, ECCV) or participation in competitions like Kaggle, COCO, ImageNet, ActivityNet, and 3D-related challenges.