IIT Gandhinagar Department of Electrical Engineering Research Assistantship 2021

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EligibilityMaster’s/Bachelor's degree holders
RegionIndia
AwardVariable awards
DeadlineClosed

This scholarship is closed for further applications. It is expected to be launched by January, 2022. You can follow this scholarship for further updates.

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About the Program

Indian Institute of Technology, Gandhinagar invites applications for IIT Gandhinagar Department of Electrical Engineering Research Assistantship 2021 from Masters/Bachelor degree holders. The fellowship is meant for the project titled, “Fast, Robust, Energy-aware In-memory Computing Architectures”. The selected candidates will receive a variable award.

Fellowship
  • IIT Gandhinagar Department of Electrical Engineering Research Assistantship 2021 DeadlineClosed
    Eligibility

    To be eligible, an applicant must -

    • Hold a Master's degree in Electrical/Electronics/Electronics and communications/Electronics and Instrumentation/Computer Science and Engineering/Computer Science or related areas.
    • Hold a Bachelor's degree in Electrical/Electronics/Electronics and communications/Electronics and Instrumentation/Computer Science and Engineering/Computer Science or related areas with valid GATE score
    Benefits

    The selected candidates will receive a variable award.

    Documents
    How can you apply?

    Eligible candidates can apply for the assistantship by following the steps mentioned below:

    Step 1: Click on the 'Apply Now' button below and read all details. 
    Step 2: Fill the application form and upload supporting documents.
    Step 3: Submit the application. 

Important Dates

Application deadline: 22nd January 2021

Selection Criteria

The selection of the candidates will be made based on the applicant's performance in the interview. 

Important documents
Terms and Conditions
  • No TA/DA will be provided for an interview.
  • The interview will be conducted online.
  • This project involves design and analysis of memory subsystem and integrating compute circuits to achieve high performance for AI/ML applications.

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