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(Freshers) "Intel" Hiring: Graduate Technical Intern On Feb 2019 @ Bangalore



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Company:        Intel

Website:         www.intel.in

Eligibility:      M.Tech/PhD

Experience:     Freshers

Location:         Bangalore

Job Role:        Graduate Technical Intern

JOB SUMMARY:

Company profile :

Intel Corporation(www.intel.in), incorporated in 1968, designs and manufactures integrated digital technology platforms. A platform consists of a microprocessor and chipset. The Company sells these platforms primarily to original equipment manufacturers (OEMs), original design manufacturers (ODMs), and industrial and communications equipment manufacturers in the computing and communications industries.

Job Description :
1. The intern will work in the area of deep reinforcement learning and develop advanced RL algorithms to enable autonomous multi-agent systems for collaborative tasks (e.g. co-operative assembly with multiple robotic arms).

2. The goals of the internship will include literature review of state-of-the-art techniques on Deep Reinforcement Learning, developing RL algorithms in python testing of models in simulators (e.g. OpenAI gym simulator, Gazebo robotics simulator etc.).

3. The candidate must have solid background and hands-on experience in the areas of Artificial Intelligence, Artificial Neural Networks, Machine Learning, Deep Learning and Reinforcement Learning, preferably exposure to robotics / autonomous systems.

Candidate Profile:
Qualification: Student pursuing M.Tech/PhD in CS, ECE, EE or related field

Required Skills:
1. Experience in Deep Learning, Reinforcement Learning and Machine Learning, Hands-on experience with ML toolkits such as Tensorflow/Pytorch/Caffe etc. ,

2. Strong programming expertise in Python and experience in using Deep learning packages such as Tensorflow and/or Pytorch

3. Exposure to robotics is an added advantage - Robotic Simulators (e.g. Gazebo, V-REP, R-Viz, OpenRave, Move-it etc.), Robotic Operating System (ROS), Forward/Inverse Kinematic models.

Desired Courses: Reinforcement Learning, Machine Learning, Optimization techniques, Linear Algebra

 













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