GE Healthcare Recruitment Drive 2019


GE Healthcare Recruitment Drive:-

About Company:-

GE Healthcare is an American multinational conglomerate incorporated in New York and headquartered in Chicago, Illinois. As of 2017, the company is a manufacturer and distributor of diagnostic imaging agents and radiopharmaceuticals for imaging modalities that are used in medical imaging procedures.

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Company Name:-  GE Healthcare

Company Website:-

Job Position:-  Intern

Job Location:-  Bengaluru, Karnataka

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  • BE/BTech in Electrical Engineering/Electronics and communication engineering/Computer science/Biomedical engineering with specialization in video analytics, medical imaging, image processing, computer vision, or inverse problems.
  • The Master’s candidate with proven research credentials in the fields of AI are also highly encouraged to apply.

Desired Characteristics:

• Strong knowledge on video analytics and medical imaging
• Must ability the ability design and code deep neural hybrid, feed forward, and feedback neural networks from first principles
• Excellent programming skills on Python, C++,and CUDA programming
• Hands on TCL scripting
• Experienced on PyCharm IDE, Jupyter notebook
• Strong Problem Solving & reverse engineering skills
• Knowledge computer vison tools such OpenCV, Open Pose
• Strong Knowledge on GPU based optimization techniques such as TesnorRT
• Must have excellent debugging and problem solving skills
• Strong GPU knowledge such as loading tensor flow/keras model into GPU, communication of model throughput to cpu, writing the GPU inferencing output to CPU memory
• Multi-threading and inter process communication using CUDA

Role Summary:

This role expects the candidate to Lead design and development of computer vision algorithms, apply supervised and unsupervised deep learning models to clinical images and work with other vision scientists and SW engineers to deliver deployable technology while seeking creative ways to streamline medical workflows.

Essential Responsibilities:

• Design and development of Artificial neural networks with transfer learning, deep learning, reinforcement learning, recurrent learning, and inverse problems
• Should also develop automations tools for annotation & labeling, training, and inferencing on camera video data and medical DICOM data
• Investigate and quantify various deep learning models for video analytics and medical image analytics

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