Google off campus drive Pre-Doctoral Researcher, University Graduate

2022-09-28 Click here to join our placement whatsapp group

Minimum qualifications:

• Bachelor's degree in a STEM field (e.g., Computer Science, Mathematics, or Statistics) or equivalent practical experience.
• Experience with Python, C/C++, or Java
• Research experience in machine learning or AI techniques (e.g., open source projects, campus lab experience, research internships, or publications).

Preferred qualifications:

• Master's degree in a STEM field (e.g., Computer Science, Mathematics, or Statistics) or equivalent practical experience.
• Ability to work and collaborate across multiple teams

The Pre-Doctoral Researcher role is a 24-month role designed to give you experience working on challenging problems together with Google research scientists and engineers in India before pursuing (or making a decision on pursuing) doctoral research.

As a Pre-Doctoral Researcher, you will collaborate with researchers, scientists, and engineers at Google Research working on fundamental research and applied problems in a wide range of areas in Computing. You will have a wide range of opportunities from conducting fundamental research to contributing to products and services used by billions of people, and conducting research that contributes to human-centered AI and catalyzes AI for Social Good. We encourage our Pre-Doctoral Researchers to publish their work externally.

Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.

Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.

Responsibilities

• Work with research mentors to formulate research projects and/or novel applications of machine learning.
• Conduct research and publish it.
• Implement algorithms, experiments, and/or human-computer interfaces using frameworks (e.g., TensorFlow).
• Learn and understand a large body of research in machine learning algorithms.
• Understand how to use research to drive design decisions.

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