PamiTC Job Board - Posting Details

Title: Summer Intern - Computer Vision and Machine Learning (Object Recognition and Scene Understanding)Posted: January 18, 2016
Company/Institution: Ricoh Innovations
Location: Cupertino, CA

Position Description:
Ricoh Innovations is seeking a highly motivated Ph.D. student researcher with a practical and broad knowledge of computer vision and machine learning. The successful candidate will collaborate with an established team of computer vision experts led by Dr. Kathrin Berkner and Prof. Savarese from Stanford University, developing novel computer vision and machine learning algorithms for object recognition and scene understanding using large sets of image data.

- Develop novel methods to solve fine-grained object recognition and 3D scene understanding problems using the latest computer vision and machine learning techniques including Deep Learning
- Collaborate with other researchers to implement efficient algorithms with direct impact to real world applications
- Document and patent results

Required Qualifications / Skills:
- Ph. D. student in Computer Science, Electrical Engineering or other degrees closely related to computer vision and machine learning
- Clear understanding of basic concepts in computer vision and machine learning
- Experience with popular computer vision/machine learning software libraries such as OpenCV, Scikit-Learn, and PyLearn2
- Experience with deep learning framework such as Caffe and Theano
- Ability to work collaboratively in an interdisciplinary team
- Excellent verbal and written communication skills

Desired Qualifications/Skills:
- Knowledge of contemporary computer vision and machine learning algorithms including convolutional networks, supervised and unsupervised learning, and statistical methods
- Experience with deploying state-of-the-art machine learning methods to application-driven computer vision problems
- Strong theoretical foundations in image processing and probabilistic graphical models
- Knowledge of 3D computer vision techniques, algorithms, and tools
- Knowledge and experience with GPU programming such as CUDA and OpenCL
- Publications in top-tier computer vision/machine learning conferences such as CVPR, ICCV, ECCV, NIPS, and ICML

Application Instructions:
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