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Description
Experience: 4+ years Requirements: Excellent knowledge of computer vision concepts, including but not limited to Image Classification, Object Detection, and Semantic Segmentation, developed using state-of-the-art deep learning algorithms. Hands-on experience developing efficient and real-time convolutional neural network (CNN) models for computer vision tasks. Strong proficiency in at least one of the deep learning frameworks, such as PyTorch, TensorFlow, or Caffe, with the ability to apply them to computer vision problems. Quick prototyping skills in Python and coding and debugging proficiency in C++. Good communication and collaboration skills to work effectively in a team and communicate complex technical concepts. Qualifications: A Ph.D. degree (including candidates at various stages of their Ph.D., such as thesis submission, thesis submitted, degree awaited, synopsis seminar completed, defense completed) in Deep Learning with hands-on coding skills and a passion for an industrial career will be preferred. Master's or Bachelor's degree with thorough industrial work experience in developing computer vision applications using deep learning. Postgraduates or Undergraduates with a strong academic background in Deep Learning, Computer Vision, or related fields, and demonstrated coding skills, are also encouraged to apply. Preferred: Publications in top-tier computer vision conferences like CVPR, ICCV, ECCV, or major AI conferences like NeurIPS. Knowledge of computer vision libraries and tools, including OpenCV and DLib, and a solid understanding of image processing and computer vision fundamentals. Hands-on experience with model compression and pruning techniques in deep learning. Good exposure to various deep learning architectures, such as Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks. Familiarity with GPU programming (e.g., CUDA, OpenCL) for efficient deep-learning computations. Pay is competitive as per market standards. Show more Show less
